esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Communication and Measurement System with BOC-Assisted OFDM
## 1 Introduction
[13] realizes the combination of the OFDM system and the BOC system; however, they do not consider the influence of the Doppler frequency offset on the OFDM system. Due to the characteristics of the subcarriers and the high-speed motion, the OFDM signal is extremely susceptible to the Doppler frequency offset [22]. The Doppler frequency offset can degrade the orthogonality between subcarriers, leading to intercarrier interference, which considerably decreases the communication performance. In this paper, we propose an integrated communication and measurement system under a Doppler frequency offset environment. The system adopts OFDM modulation to achieve a high transmission rate and BOC modulation to achieve high-precision ranging. We utilize the orthogonality of the OFDM subcarriers and the spectral characteristics of BOC modulation to formulate an OFDM+BOC composite signal through power control. Furthermore, we adopt the high-precision Doppler frequency offset tracked by the BOC signal to assist in the demodulation of the OFDM signal. This approach can considerably reduce the impact of the Doppler frequency offset on the OFDM signal. Moreover, a significant improvement in the measurement accuracy is achieved. The overall structure of this paper is divided into six sections, including this introductory section. Section 2 begins by introducing the structures of the transmitting and receiving systems and discusses how to design the composite signal. Section 3 presents the process of receiving the composite signal, focusing on three key aspects: acquisition, tracking of the BOC system, and demodulation of the OFDM system. Section 4 examines the performance of the composite signal. Sections 5 and 6 present a discussion and conclusions regarding the composite signal.
# MOCA: A Network Intrusion Monitoring and Classification System
## 4 MOCA Design  
MOCA consists of a two-step approach: (1) monitor and detect anomalies, and (2) classify attacks. Figure 1 depicts the framework of MOCA. Instead of training one ML/DL-based classifier, MOCA trains a binary classifier and a multi-class classifier. The binary classifier is to distinguish abnormal traffic from normal traffic. It is necessary to mention that all traffic is available prior to feature calculations. The multi-class classifier performs multi-class classification on the abnormal traffic to identify attack classes.  
Two separated feature subsets are used for training classifiers. First, a small set of features is selected for a binary classifier to identify abnormal traffic. The second feature subset is selected for a multi-class classifier to identify attack classes. Various feature selection methods can determine the two feature subsets. The rest of this section focuses on the feature selection methods.  
Figure 1: Framework of MOCA.
## 2 Design Issues  
### Related Works  
There has been a certain amount of research projects on a protocol bridge between sensor networks and the TCP/IP network. [PERSON] _et al._[6] classified these research projects into a Gateway-based approach and an Overlay-based approach. In the Gateway-based approach, there are application-level gateways [6; 11; 12] and a Delay Tolerant Network [13]. The application-level gateways have a role to translate protocols of both networks and to relay data packets. Since the protocol translation only occurs within the gateways, this approach can easily support heterogeneous sensor networks. However, the approach cannot offer a direct access to a specific sensor node. Unlike other gateway-based research, the Delay Tolerant Network proposed a Bundle Layer residing on both network sides and a gateway. This Bundle Layer hides the differences of protocols and it enables easy integration of heterogeneous sensor networks. However, the installation of the Bundle Layer can be a critical burden to resource-scarce sensors.  
The overlay-based approach suggests overlaying protocol stacks on the sensor network [7; 8; 15] or the TCP/IP network [14]. 6 LoWPAN [15] and u-IP [7] insist that additional protocol stacks on sensor nodes can resolve the integration problem and provide the end-to-end communication. However, this approach causes constant controversy because the installation of protocol stacks and the protocol translation overhead can go beyond typical sensor's capability. On the other hand, [14] proposed an installation of additional protocol stacks on the TCP/IP network. It has the same advantage like other overlay-based approaches, but it greatly hurts the consistency and the interoperability of the TCP/IP network.  
In summary, existing research projects have mainly focused on providing the node-centric communication which enables the end-to-end communication between two heterogeneous networks. However, these all projects lack in efficient reuse of sensing data and a highly sophisticated data access based on the data-centric manner.
## 4 Feature Extraction and Selection
### Target Variables  
Target variables represent the values to predict, such as the next offset to be read and the hotness of a file in the near future. Accurate prediction of these two targets can drive caching and tiering policies into making important decisions that optimize the performance of multi-tier storage systems. Next offsets can be used for prefetching data into the cache proactively, thereby reducing the access latency of future read requests. File hotness is a proxy of how popular a file will become, which is crucial when making cache eviction, cache admission, and tier migration decisions. The target variables are also calculated and added as a target column to the feature vectors to generate training instances.  
The _Next Offset_ target variable is a numeric value, and a natural solution is to model it as a regression. The advantage of doing so is that, if accurate enough, it can determine the next portion of the file to be accessed, helping a prefetching policy to make good decisions. However, a moderate or even a small error when predicting the next offset may mislead to different regions of the file. This motivates us to also model the prediction of the Next Offset as a 3-class classification problem by determining if (1) the next offset is continuous to the current offset, (2) the next offset is at a random file location, or (3) the file will not be read anymore; labeled as _sequential_, _random_, and _none_, respectively. Classification is a relaxation of the regression problem, yet provides sufficient information to help prefetching policies to take action in the presence of primarily sequential workloads.  
The _Next File Hotness_ is also a numeric value (computed using Equation (6)), represents the hotness of a file during a future time interval, and can be modeled using either regression or multi-class classification. The prediction of the next file hotness as a regression has the advantage of differentiating how hot (or cold) a file is, which can help cache and tiering policies to take actions, such as prioritizing files during data movement. Contrary to the prediction of the next offset, a small or moderate prediction error may not affect determining how hot the file will be in the near future. This motivates modeling the prediction of the next file hotness as a classification problem. We categorized the file hotness into six distinct classes, with the first class being the coldest and the sixth the hottest. The number of classes was determined based on the distribution of the normalized values of file hotness across all traces.  
Overall, we modeled both problems as regression and classification to analyze their performance and differences, computing four target variables: _Next Offset_ and _Next Offset Class_ for predicting the next file offset with regression and classification, respectively, and _Next File Hotness_ and _Next File Hotness Class_ for predicting the future file hotness with regression and classification, respectively.
## 3 The PFAC Measurements and the FRM Uncertainty Analysis
### Uncertainty Budget for Sea-Surface Calibration
#### 3.2.1 Sea-Surface Height Constituent
Monitoring of the long-term performance of all tide gauges with an external and independent system without interrupting their measurements poses a challenge in the PFAC operations. Two approaches are followed: In the first, the monthly means of each tide gauge measurement are monitored to verify their overall performance. The second uses an external reference instrument (i.e., tide pole) for sea-surface height determination. A leveling survey is carried out to tie the tide pole and tide gauge reference points. The Van de Casteele diagrams are again created, but using the tide pole as reference. This procedure is carried out at least once per year and a detailed record is kept. If the difference between the tide pole and one of the tide gauges exceeds certain criteria (i.e., 1 cm), then the external validation is repeated within two months. If the difference continues to exceed the criteria set, then the tide gauge is decommissioned and sent to external facilities for calibration. This technique is also used to precisely estimate the electrical center, i.e., the measuring reference point (\"zero\" point) of the tide gauge records.
# Unlocking Everyday Wisdom: Enhancing Machine Comprehension with Script Knowledge Integration
## 2 Background and Related Work
### Integrating Commonsense Knowledge  
#### 2.2.1 Using Knowledge Graphs  
Knowledge graphs are broadly used [8; 9; 10; 11] as a source of commonsense knowledge. These large graphs consist of nodes representing concepts and edges describing the relationships between them. A representative example is ConceptNet [12], which is a freely available, multi-lingual semantic network. ConceptNet comprises triplets of (subject, relation, object), e.g., (\"apple\", \"IsA\", \"fruit\"), (\"plane\", \"CapableOf\", \"fly\"), and (\"car\", \"Causes\", \"pollution\"). The subject and object are multi-word phrases, and the relation is one of the relation types. ConceptNet contains \(\sim\)630 k of such triplets and 39 types of relationships. An overview is shown in Figure 2.  
Figure 1: An example passage from the SemEval 2018 dataset, and two corresponding questions.  
Figure 2: An overview of ConceptNet [12].  
ConceptNet is one of the most extensively used commonsense knowledge graphs. For example, ref. [8] generated knowledge embeddings from ConceptNet to enhance the context features for cloze-style QA tasks. Ref. [9] used ConceptNet data to train a network that predicts the relevance score of concepts, thereby assessing the relevance of a candidate answer to a given question in context. Ref. [11] devised two auxiliary training tasks to improve machine reading comprehension, thereby predicting the existence and type of relationships between concepts in provided texts. The ground truth for these sub-tasks has been sourced from ConceptNet.  
We also applied ConceptNet in our experiments to acquire the relationship information of concepts, thereby serving as an input for our model. Larger and richer knowledge graphs may bring further improvements, e.g., the Google Knowledge Graph, which is a proprietary knowledge base developed by Google to enhance its search results and other services. It can connect more complex information to answer questions, such as \"Who is the president of the country where the White House is located\". Our method is fully compatible with more advanced knowledge graphs, which we leave for future work.
# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
## 3 System Model
### Path Loss Model  
In the paper, we consider task offloading for MEC-assisted platooning in a wireless cellular network. The communication mode of platoon members is Vehicle to Vehicle (V2V) communication, which is deployed according to IEEE 802.11p standard [28]. The communication mode between vehicle and BS is Vehicle to Infrastructure (V2I) communication, which is deployed according to LTE-V [29].  
When one vehicle is served by another, the path loss \(\mathrm{PL}_{v}(l_{v_{1},v_{2}})\) between the vehicle \(v_{1}\) and vehicle \(v_{2}\) is modeled by Reference [30]  
\[\mathrm{PL}_{v}(l_{v_{1},v_{2}})=63.3+17.7\log_{10}(l_{v_{1},v_{2}}) \tag{1}\]  
where \(l_{v_{1},v_{2}}\) is distance in kilometers between two vehicles. Additionally, the path loss \(\mathrm{PL}_{\mathrm{MEC}}(l_{q,u})\) between the leader and BS is modeled by Reference [31]  
\[\mathrm{PL}_{\mathrm{MEC}}(l_{q,u})=128.1+37.5\log_{10}(l_{q,u}) \tag{2}\]  
where \(l_{q,u}\) is the distance in kilometers between the leader and BS, \(q=1,\ u=0\) or \(q=0,\ u=1\). Here, for convenience of discussion, we do not consider fast fading and shadow fading in our wireless communication model. The carrier frequency used in V2I communication is 2 GHz and V2V communication is 5.9 GHz. Therefore, there is no interference between V2I and V2V communication. The distances between vehicles in the platoon and the distances between the platoon and BS are illustrated in Figure 4. The MEC server is numbered 0, the platoon leader vehicle is numbered 1, and the sequence number of vehicles following it increases in turn. \(l_{v_{1},v_{2}}\) is the geographic distance from \(v_{1}\) to \(v_{2}\), where \(v_{1}\) and \(v_{2}\) are the vehicle terminal numbers, \(v_{1},\ v_{2}=1,\ 2,\ \ldots,\ m\) and \(v_{1}\
eq v_{2}\).  
Figure 4: Illustration of the distance of platooning and base station (BS).  
Figure 3: Task model in a linear topology.  
Considering that under ideal conditions all the vehicles in the platoon are of the same length and the same spacing so that the distance between the signal transmitter (\(v_{1}\)) and the receiver (\(v_{2}\)) in the platoon is the length of the vehicle plus the vehicle spacing. Then we get  
\[l_{v_{1},v_{2}}=\mu|v_{1}-v_{2}| \tag{3}\]  
where \(\mu\) is the distance in kilometers between the adjacent transmitter and the receiver. \(|v_{1}-v_{2}|\) is the number of intervals. Specially, considering offloading tasks to the MEC server, and the distance in kilometers between the leader and the BS is set to \(\eta\), we obtain  
\[l_{1,0}=\eta. \tag{4}\]  
When the task is offloaded to the MEC server, the change of distance between the leader and the BS has negligible effect on the path loss, so we get \(l_{1,0}=l_{0,1}\).
## 1 Introduction
The objective of the present study is to introduce an approach that specifically detects soil tillage operations, such as tillage, at the parcel level in Portugal. This approach utilizes both Sentinel-1 and Sentinel-2 data, employing a temporal change detection algorithm applied to S1 cross-polarized (VH) backscatter. The method is implemented in bare or scarcely vegetated fields, identified through a thresholding of the S2 Normalized Difference Vegetation Index (NDVI), where the S1 cross-ratio (CR-VH/VV ratio) can be adopted as a proxy when the NDVI is unavailable, by comparing rule-based and machine learning approaches. Additionally, this approach is adaptable for use in other regions with appropriate adjustments, making it broadly applicable to agricultural monitoring efforts.
## 2 Modeling of Fuel Cell Systems
### Fuel Cell Single Cell Modeling
#### 2.1.3 MEA Temperature Model  
MEA consists of a gas diffusion layer, catalyst layer, and proton exchange membrane layer, which is the core of a fuel cell. In an MEA, both ends are bipolar plates, and both ends of the heat exchange mode have heat conduction and heat convection [22]. Its advantage over fossil fuels is that it can be converted directly from chemical energy to electricity [23]. During the operation of a fuel cell, the catalyst inside the layer will undergo a chemical reaction, and the main heat production of the layer is related to this. The energy conservation equation of MEA is:  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Name** & **Value** \\ \hline \(\mathrm{\mu_{gas}}\) & Aerodynamic viscosity (air) & \(1.71\times 10^{-5}\) Pa\(\cdot\)s \\ \(\mathrm{Pr_{gas}}\) & Prandtl constant (air) & 0.708 \\ \(\mathrm{k_{gas}}\) & Conductivity coefficient (air) & 0.0263 W/m\({}^{2}\cdot\)K \\ \(\mathrm{L_{p,liq}}\) & Length of liquid runner & 381 mm \\ \(\mathrm{P_{cs,liq}}\) & Liquid runner section perimeter & 7.62 mm \\ \(\mathrm{A_{liq}}\) & Section area of liquid runner & 3.23 mm\({}^{2}\) \\ \(\mathrm{\rho_{liq}}\) & Liquid density (water) & 1000 kg/m\({}^{3}\) \\ \(\mathrm{\mu_{liq}}\) & Hydrodynamic viscosity (water) & 3.79 \(\times\) 10\({}^{-4}\) Pa\(\cdot\)s \\ \(\mathrm{Pr_{liq}}\) & Prandtl constant (water) & 7 \\ \(\mathrm{k_{liq}}\) & Conductivity coefficient (water) & 0.602 W/m\({}^{2}\cdot\)K \\ \multicolumn{2}{c}{The number of electrons exchanged by} \\ \multicolumn{2}{c}{n} & chemical reactions \\ \multicolumn{2}{c}{F} & Faraday constant & 96485 C/mol \\ \(\mathrm{m_{rv}}\) & Coolant mass (Water tank) & 2.5 kg \\ \(\mathrm{k_{rv}}\) & Heat transfer coefficient (coolant, Tank wall) & 0.64 W/m\({}^{2}\cdot\)K \\ \(\mathrm{m_{hs}}\) & Coolant mass (radiator) & 0.2 kg \\ \hline \end{tabular}
\end{table}
Table 1: Cont.  
\[\begin{array}{c}\left[c_{\rm p,avg}(m_{\rm gas}+m_{\rm liq})+(\rho_{\rm L3}A_{ \rm L3}h_{\rm L3}c_{\rm p,L3})\right]\frac{\rm dT_{\rm L3}}{\rm dt}\\ =Q_{\rm L2}+Q_{\rm L2R}+Q_{\rm int3}+Q_{\rm L4}+Q_{\rm L4R}+\Delta H_{\rm liq}+ \Delta H_{\rm gas}\end{array} \tag{15}\]  
The calculation formula of \(Q_{\rm L2},Q_{\rm L2R},Q_{\rm L4},Q_{\rm L4R},\Delta H_{\rm liq}\) and \(\Delta H_{\rm gas}\) is similar to that in Section 2.1.2. The heat production in an MEA can be calculated by the following formula:  
\[Q_{\rm int3}=\frac{\rm iT_{\rm L3}\Delta S}{h_{\rm L3}nF}+V_{\rm act} \tag{16}\]  
In Formula (16), \(\Delta S\) is the entropy change when the reactants use the catalyst to produce products; n is the number of electrons exchanged by the chemical reaction; and F is Faraday's constant.  
The heat exchange model of each layer of the fuel cell is shown in Figure 3.
## 5 Data Collection Experiment and Dataset Description
### Dataset Description  
In this experiment, the radar was positioned at a height of 1.08 m above the ground. The wall material was reinforced concrete with a thickness of 0.26 m. We designed a total of 9 actions, as described in Table 2. A key measure of a model's ability to recognize actions is its capability to distinguish between similar actions. Therefore, the actions we designed included groups of similar actions. Among them, walking and running constituted one group; punching, kicking, and stepping constituted another group; and bending, sitting, squatting, and standing up constituted the third group.  
Figure 9: The experimental setup for through-wall radar human activity recognition in an indoor environment.  
Volunteers were recruited for the experiment, totaling 12 participants. Each participant performed each action 20 times, resulting in 240 samples for a single action. In total, 2160 samples were collected for all actions. In a single sample, the radar data were represented as a matrix with a vertical dimension of slow time, totaling 960 timestamps, and a horizontal dimension of fast time, totaling 480 sampling points. The slow time dimension represents actual time, while the fast time dimension represents distance. This form of matrix, after applying the clutter suppression methods mentioned earlier, resulted in the RTM used in this study.
## 4 MEMAC Algorithm Design
### Analysis of Game Probabilities  
Since pure-strategy Nash equilibria can lead to local optimal solutions, it is necessary to consider the equilibrium states of mixed strategies. In this paper, the choice of Nash equilibrium probabilities for different strategies within the probability distribution \(P_{R}=P_{a},P_{b}\) of multipath channel information is permitted to adapt to the complex underwater multipath channels while ensuring a Nash equilibrium in the global state.  
The probability distribution \(P_{R}\) can be expressed as follows:  
\[\text{P}_{R}=\left\{\begin{array}{l}R=R_{1}\big{(}P_{a}^{1},P_{b}^{1}\big{)} \\ R=R_{2}\big{(}P_{a}^{2},P_{b}^{2}\big{)}\\ \ldots\ldots\ldots\\ R=R_{n}\big{(}P_{a}^{n},P_{b}^{n}\big{)}\end{array}\right. \tag{21}\]  
where \(P_{a}^{1}\) is the probability that node i selects policy PCH under multipath channel \(R_{1}\), and \(P_{b}^{1}\) is the probability that node i selects policy NCH under multipath channel \(R_{1}\). Further, the expected utility of node i choosing PCH strategy can be expressed as follows:  
\[U_{i}^{\text{expect}}\left(S_{i}=PCH\right)=Re^{-\varphi}\Big{(}E_{i}^{\text{ res}}-C_{i}^{PCH}+\beta W\Big{)} \tag{22}\]  
Similarly, the expected utility of node i choosing the NCH strategy can be expressed as follows:  
\[\begin{split} U_{i}^{\text{expect}}\left(S_{i}=NCH\right)\\ &=\big{(}2-R_{i}e^{-\varphi}\big{)}(E_{i}^{\text{res}}-C_{i}^{ ON}\\ &-\gamma_{i})\bigg{(}1-\Big{(}1-P_{a}^{i}\Big{)}^{N-1}\bigg{)}\end{split} \tag{23}\]Since each node has the same expectation for selecting PCH and NCH policies, we get the following:  
\[U_{i}^{\text{expect}}\left(S_{i}=PCH\right)=U_{i}^{\text{expect}}\left(S_{i}=NCH\right) \tag{24}\]  
When it is combined with Formulas (22)-(24) above, we can get the following:  
\[P_{a}^{i}=1-\left(1-\frac{\left(E_{i}^{res}-C_{i}^{PCH}+\beta W\right)}{\left( 2-R_{i}e^{-\varphi}\right)\left(E_{i}^{res}-C_{i}^{ON}-\gamma_{i}\right)} \right)^{N-1} \tag{25}\]  
Let (\(2-R_{i}e^{-\varphi}\)) in the above equation = \(\epsilon\), \(\left(E_{i}^{res}-C_{i}^{PCH}+\beta W\right)=\omega_{1}\), \(\left(E_{i}^{res}-C_{i}^{ON}+\gamma_{i}\right)=\omega_{2}\); then,  
\[P_{a}^{i}=1-\left(1-\frac{\omega_{1}}{\varepsilon\omega_{2}}\right)^{N-1} \tag{26}\]  
From Formula (26), the probability of node i choosing NCH can be obtained:  
\[P_{b}^{i}=1-P_{a}^{i}=\left(1-\frac{\omega_{1}}{\varepsilon\omega_{2}}\right) ^{N-1} \tag{27}\]  
In sum, all nodes can obtain the probability of choosing the PCH or NCH strategy under the condition of equal expected probability through their own multipath channel state information, that is, the state of Nash equilibrium in which no node has the motivation to change strategies.
# Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use
## 4 Results and Discussion  
### PHY Performance  
#### 4.1.1 Computer Simulation Parameters  
This subsection describes the performance evaluation by computer simulations of the PLCP header detection, packet error ratio and energy efficiency in the SmartBAN PHY. Since this research did not consider the performance between the operator and the hub, the performance of the body area network was the only focus. The main parameters of the computer simulations are listed in Table 5. The computer simulator was constructed by MATLAB. Then, the comm. PreambleDetector system object in MATLAB was used for preamble detection, and the detection threshold was set to the length of each SFD minus one (\(L_{SFD}-1\)). The entire two-octet preamble was only correlated for the SmartBAN, and the threshold was set to the length of the preamble minus one (\(L_{preamble}-1\)). Table 6 summarizes the SFDs used in the computer simulations. The reason for choosing these sequences was that they could be handled in units of octets. Table 7 also lists the parameters of the IEEE model CM3 in the hospital room case.