esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Log Parsing for Monitoring Industrial ICT Systems
## 6 Evaluation and Results
### Performance of the Offline Parser  
In this set of experiments, we trained our offline parser and all comparatives for three rounds on all 18 selected datasets and conducted _log parsing for event identification  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Logs** & **Log Size** & **Events** & **Description** \\ \hline HDFS & 11197705 & 25 & Hadoop runtime log \\ \hline BGL & 4747963 & 155 & HPC Blue Gene/L runtime log \\ \hline Spark & 98671134 & 41 & Spark runtime log \\ \hline Apache & 1643182 & 28 & Apache server access log \\ \hline UofS & 2408625 & 9 & \begin{tabular}{c} University of Saskatchewan \\ WWW server access log \\ \end{tabular} \\ \hline Jul95 & 1891714 & 7 & \begin{tabular}{c} NASA Kennedy Space Center \\ WWW server access log \\ \end{tabular} \\ \hline Nginx & \begin{tabular}{c} 1067357 \\ 1067459 \\ 1067357 \\ \end{tabular} & \
\begin{tabular}{c} 4 \\ 2 \\ 20 \\ \end{tabular} & Nginx server access log \\ \hline Openstack & 189386 & 17 & Openstack runtime log \\ \hline Security Log & 22694356 & 10 &
\begin{tabular}{c} Connection log from \\ Security Data Analysis Labs \\ \end{tabular} \\ \hline Thunderbird & 211212192 & 40 & HPC Thunderbird runtime log \\ \hline Big Brother & 68666 & 8 & Big Brother diagnostic log data \\ \hline Web Log & 1125760 & 15 & Security Repo access log \\ \hline ThingWorx & 1849361 & 12 & ThingWorx platform log \\ \hline
4 SICS-151020 & 246137 & 15 & 4 SICS Geek Lounge Pcaps \\ \hline
4 SICS-151021 & 1253100 & 19 & 4 SICS Geek Lounge Pcaps \\ \hline
4 SICS-151022 & 2274747 & 29 & 4 SICS Geek Lounge Pcaps \\ \hline EMSD & 5764522 & 68 & Energy Management System log \\ \hline IoT sentinel & 129371 & 19 & IoT devices’ captures \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of evaluation datasets.  
based on the best performing model from each log parsing method, respectively. Unless specified, the training set drawn from each dataset contained 200 randomly selected log records labeled with each type of event. If the number of records of a specific type of event was less than 200, all records were used.
## 5 Results Using Test Data Obtained with FEM Model
### Verification of Results  
For verification of the results, an FEM model was made, based on the soil determination procedure presented in Section 3.2.  
The basic model, together with the determined model, is presented in Figure 13.  
The basic model (Figure 13a) is presented in detail in Figure 5. The determined FEM model presented in Figure 13b was obtained after the soil-determined procedure. The soil parameters in each subarea were calculated using the ABC method and are presented in Tables 5-13. In both FEM models, the Wenner measurement was simulated at the lines marked Meas. A to Meas. F, which are presented in Figure 13a,b. The differences were determined using the expression for OF written in (8). The obtained differences were as follows: 16.1% for Meas. A, 18.9% for Meas. B, 13.1% for Meas. C, 19.1% for Meas. D, 8.5% for Meas. E, and 6.2% for Meas. F. Although the differences were up to 19%, it should be noted that the mean OF calculated for different subareas (Tables 5-13) was also over 6%. To show that the presented differences are acceptable, the apparent resistivity for the Basic and Determined models at lines Meas. A to Meas. F is presented in Figure 14.  
Figure 14: Apparent resistivity at lines (**a**) Meas. A, (**b**) Meas. B, (**c**) Meas. C, (**d**) Meas. D, (**e**) Meas. E and (**f**) Meas. F.  
Figure 13: (**a**) Basic and (**b**) determined FEM model.  
From Figure 14, the comparability between the apparent resistivity obtained from the basic model and apparent resistivity obtained from the determined model can be seen. Based on this, the accuracy of the suggested method for soil modeling can be confirmed in the case of inhomogeneities.  
The test calculations were made with a cube, but, in reality, strange forms of geological anomalies can occur in the ground, as shown in Figure 15a.  
The geological anomaly influences the Wenner measurement, which is performed on the lines above it, as shown in Figure 15a. Based on the Wenner measurements, an FEM model of the soil was created consisting of nine regions, each of which is defined by a different three-layer soil model, as shown in Figure 15b. In this approach, any anomaly that affects the Wenner measurement is captured.
## 3 Application Protocol for Wireless Intelligent Sensor Networks
### Sensor Multi-Agent Structure  
The software of the sensors was designed based on a multi-agent structure, which is shown in Figure 1. The software is composed of three agents: management, application control, and communication. The management agent allows the system to turn up or turn down services or devices in the sensors and execute other agents. The control agent executes different applications in the sensor, such as measurements, actions, FRBSs, and collaborative FRBSs. Finally, the communication agent allows sensors to communicate with other sensors and with a computer connected to the WSN by means of an access point.  
The following sections present the agent objectives and describe their functions.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 4 Proposed Work
### Optimized Beam Training for Adaptive mmWave Communication  
The \"digital and analog beam training with long short-term memory (DAH-BT-LSTM)\" network is utilized to further improve performance. By examining the signals received from earlier training sessions, this technique improves the beam direction and makes it easier for the system to forecast and modify the beam direction. DAH-BT-LSTM lowers training expenses, increases noise resilience, and lessens the need for regular recalibration by utilizing both long-term and short-term signal patterns. To provide the greatest possible signal quality and performance, the beam is constantly aligned thanks to this ongoing adjustment and optimization process.
## 5 Discussion
### Evaluation of the Machine Learning Models  
The findings demonstrate that DT, NB, and KNN attained superior classification accuracy, surpassing 85% in both the K-fold cross-validation (K = 10) and train/test splitting methodologies. The highly regarded performance of these models indicates their appropriateness for lung cancer classification, presumably owing to their capacity to manage the intricate connections among features. The findings correspond with previous research that has shown the efficacy of these models in lung cancer prediction [3,14].  
RF, AB, and LR showed strong performance, with accuracy rates similar to DT, NB, and KNN. However, SVM and LR had a slightly lower accuracy, likely due to their sensitivity to feature selection and hyperparameter optimization.
## 4 Southern Cluster's BUC, Products, and KPIs
### BUC and KPIs for the Demonstration of the F-Channel in Greece  
The Business Use Case (BUC) for the F-channel platform is \"Enhanced severe weather condition management and outage management for TSO, DSO, and microgrid operator\", and it will be shown through the Greek demonstration. In cooperation with OneNet partners, this BUC has been connected with products and System Use Cases (SUCs) (Table 1).
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 5 Experiment Evaluation
### Guest Network Service Porting  
Once all the parties are authenticated (i.e., UAV-UE and UAV-Guest eNB), the UAV can send the guest eNB info using newly created _tower_info_ message passing the _phy_cell_t_ data structure and _token_ as parameters to the UE. The _phy_cell_t_ data structure acts as an identifier for the eNB and the _token_ acts as the new concealed identity for this UE. Moreover, the _phy_cell_t_ structure contains the pci, eerfcn, and _cto_hz_ parameters, where the pci and eerfcn are enough to identify the tower.  
The UE starts the service porting connection to the guest eNB by sending connection request using the _send_con_request_ function from the _srsue: rrc_ module. The _send_con_request_ function sends the IMSI/TMSI and the _cause_ as identification parameters. Based on the _cause_, the eNB treats the incoming request either as a normal request or porting request. In case of porting request, we specify the cause as _porting_ and insert the token instead of the IMSI/TMSI. Supposing the token provided by the UE matches the one generated by the eNB in the accommodation step. In that case, the guest eNB accepts the UE porting request and continues the connection procedure as a regular user without knowing the user's actual identity. The RRC connection follows the same procedure as the regular RRC connection with one change; the UE uses the token as a concealed ID, CID, rather than its IMSI/TMSI at the connection request.
# Design of Triple-Band (DSRC, 5G, 6G) Antenna for Autonomous Vehicle Telematics
###### Abstract  
A triple-band stacked-patch antenna covering dedicated short-range communication (DSRC), fifth-generation (5G) millimeter-wave, and sixth-generation (6G) millimeter-wave frequency bands is reported for autonomous vehicles telematics applications. To show the effectiveness of the developed antenna, the antenna performances, such as the S-parameter, realized gain at boresight, and radiation patterns were simulated and measured for DSRC and 5G, and only simulated for 6G. The simulated results show good agreement with the measured results. The results show that the developed triple-band antenna can cover all three bands with high peak realized gains of 6.87 dBi, 12.3 dBi, and 19.8 dBi at DSRC, 5G, and 6G frequency bands, respectively, as well as high isolation between ports of >20 dB. The results also show a straightforward structure and higher antenna gain than the previously reported triple-band antennas in the simulation level.  
patch antenna array; telematics; triple-frequency +
Footnote †: journal: Electronics Letters
# Development of the First Portuguese Radar Tracking Sensor for Space Debris
## 4 Waveform Design  
Designing a proper waveform for the radar pulse is of utmost importance. By manipulating the transmitted pulses in terms of amplitude, frequency, and phase, it is possible to create waveforms that maximize the range and velocity resolution of the echo signal. In order to evaluate the characteristics of a waveform, one can use the ambiguity function (AF). The AF represents the time response of the matched filter when the signal received is affected by a delay \(d\) and a Doppler shift \(v\) relative to the values expected by the filter and is given by:  
\[|X(d,v)|=\bigg{|}\int_{-\infty}^{\infty}u(t)u^{*}(t+d)e^{\jmath 2\pi vt}dt\bigg{|} \tag{2}\]  
where \(u\) is the complex envelope of the signal [38]. By analyzing the zero delay and zero Doppler cuts on the AF, it is possible to define the range and velocity resolution. Since ATLAS will work with a low pulse repetition frequency when detecting debris, waveform design focuses on maximizing range resolution instead of velocity.  
Matched filtering is the most basic form of signal processing commonly used in radar. It consists of the convolution of the received signal with a filter that is matched with the emitted signal. Matching will result in the maximum attainable SNR at the output of the filter when the signal to which it was matched, after addition of white noise, is passed through it [39].  
As defined in Section 3.1, ATLAS will use pulses of 3.3 ms with a pulse repetition frequency of 30 Hz in order to maintain the desired SNR. The waveform used can be arbitrary, within the hardware limits described previously and as long as those power requirements are met.
# A Novel Non-Coherent \(Q\)-Ary TH-PPM Transceiver
## 6 Conclusions  
In this paper, we propose a new \(Q\)-ary TH-PPM transceiver, which can achieve better BER performance than the conventional \(Q\)-ary TH-PPM transceiver under non-ideal synchronization conditions. The signal structure of the new TH-PPM is combined with the time-hopping sequence and PPM, resulting in a certain degree of orthogonally between different TH-PPM symbols. Then, a new TH-PPM receiver composed of \(Q\) parallel matched filters is proposed. The received signal is simultaneously sent through the \(Q\) parallel matched filters. And when there is a correlation peak output \(kth\) matched filter (\(k\in[1,Q]\)), the data symbol can be determined to \(k\).  
The decision flow of the conventional TH-PPM receiver and the new TH-PPM receiver are introduced, respectively. The BER performances are analyzed under ideal synchronization conditions and non-ideal synchronization conditions, both for the conventional TH-PPM receiver and the new TH-PPM receiver. The results show that the new TH-PPM can achieve much better BER performance than the conventional TH-PPM under non-ideal synchronization conditions. Numerical simulations are proposed to verify the analysis of the BER performance. So, the new TH-PPM transceiver can achieve good performance when the synchronization is non-ideal, which is beneficial to implement.  
Although the new TH-PPM transceiver can reduce the time synchronization requirements, the complexity of the receiving device will increase significantly with the increase of \(Q\), which is equal to the number of Match Filters. When \(Q\) is small (such as \(Q=4\)), it seems that the complexity is acceptable. And we are striving to find ways to reduce the complexity when \(Q\) is large. Also, the MUI will be considered in future work.  
Conceptualization, P.W. and X.S.; methodology, P.W.; validation, P.W. and J.T.; formal analysis, D.L. and P.F.; investigation, P.W.; writing--original draft preparation, P.W., D.L. and P.F.; writing--review and editing, J.T. and X.S.; visualization, P.W.; supervision, J.T.; project administration, X.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Data are contained within the article.
## 4 Results and Discussion
### Geophysical Logging Response Characteristics of Coal Quality
#### 4.3.6 Logging Response Characteristics of Calorific Value  
The calorific value exhibits a non-monotonic relationship with apparent resistivity, initially increasing before reaching a peak and subsequently decreasing. A similar unimodal response pattern is observed with bulk density, where calorific value rises to an optimum before declining. Conversely, calorific value demonstrates significant negative correlations with laterolog resistivity, short-spaced density log (gamma-gamma) readings, natural gamma ray intensity, and sonic transit time.  
Coal calorific value declines with increasing ash and moisture content, where ash exerts a growing influence as metamorphism advances, while moisture's impact diminishes [90]. Calorific value initially rises before decreasing with apparent resistivity (Figure 11a). In low- to medium-rank bituminous coals, reduced inorganic minerals and moisture elevate resistivity [63,91], compounded by deep burial depths and well-developed pore-fracture systems in the study area, further increasing resistivity [47,51,68].  
Figure 10: Relation between total sulfur content in coal and logging parameters. (**a**) The relationship between total sulfur content and apparent resistivity; (**b**) The relationship between total sulfur content and lateral resistivity; (**c**) The relationship between total sulfur content and bulk density; (**d**) The relationship between total sulfur content and natural gamma; (**e**) The relationship between total sulfur content and short-spaced gamma-gamma; (**f**) The relationship between total sulfur content and acoustic transit time.  
Calorific value similarly peaks and then declines with density (Figure 11c), influenced by coal rank, mineral content, lithotype, and reservoir properties [53,74]. Progressive coalification reduces moisture, enhancing calorific value alongside density in bituminous coals [14,64]. However, as bituminous coal transitions to higher ranks, density rises while calorific value decreases [91,92].  
Calorific value negatively correlates with lateral resistivity, short-spaced gamma-gamma (CGS), natural gamma (GR), and acoustic transit time (AC) (Figure 11b,d-f). Heat distribution across macroals follows liptinite > vitrinite > inertinite, driven by liptinite's higher oxygen content [55]. Resistivity decreases as coal lithotype transitions from dull to bright coal. Concurrently, declining inorganic minerals and adsorbed radioactive elements reduce calorific value as GR increases [14,25]. Coal conductivity arises from ionic conduction in mineralized water and electronic conduction via free radicals [93]. Advancing metamorphism in mid- to low-rank coals reduces moisture, densifies coal structures, and lowers lateral resistivity and AC [77,94].
# Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation
## 3 Gibbs Sampling for MIMO Detection
### The Complexity of Gibbs Sampling  
For the sake of implementation (to limit complexity), a sequential rather than a reversible MCMC detector will be considered. The only difference resides in the way the updated position index \(j\) is chosen. In sequential detection, each iteration consists of a block iteration, where the \(n\) indices are updated sequentially, hence the name.  
Denoting the difference at iteration \(t\) as \(\mathbf{d}^{(t)}=\mathbf{y}-g\mathbf{H}\mathbf{x}^{(t)}\) and in order to further reduce complexity, one may notice that only the \(j\)th symbol is changed when (6) is computed [22]. Consequently, \(\mathbf{d}^{(t)}\) can be expressed as:  
\[\mathbf{d}^{(t)}=\mathbf{d}^{(t-1)}-g\mathbf{h}_{j}\Delta x_{j|\omega}, \tag{9}\]  
where \(\Delta x_{j|\omega}=x_{j|\omega}^{(t)}-x_{j|\omega}^{(t-1)}\) and \(\mathbf{h}_{j}\) is the \(j\)th column of \(\mathbf{H}\). Therefore, the complexity of computing the norms in (6), when the \(j\)th symbol is changed, is \(2n\) complex operations (\(n\) multiplications for each of the products \((\mathbf{d}^{(t)}))^{T}\mathbf{d}^{(t)}\) and \(\mathbf{h}_{j}\Delta x_{j|\omega}\)). Further, and assuming that the number of required iterations will be large (processing for massive MIMO systems), the complexity of an MCMC detector can be reduced even further. This can be achieved by means of a QL factorization of the channel matrix, \(\mathbf{H}=\mathbf{QL}\) and by rewriting (3) as [32]  
\[\hat{\mathbf{x}}=\arg\min_{\mathbf{x}\in\mathcal{A}^{n}}||\hat{\mathbf{y}}-g \mathbf{L}\mathbf{x}||^{2}, \tag{10}\]  
where \(\hat{\mathbf{y}}=\mathbf{Q}^{T}\mathbf{y}\). Exploiting the fact that \(\mathbf{L}\) is a lower triangular matrix, the first difference \(\mathbf{d}^{(0)}=\hat{\mathbf{y}}-\mathbf{L}\mathbf{x}\) costs \(n+2\sum_{i=1}^{n}i=n(n+2)\) multiplication and addition operations (which is lower than the conventional model \(2n^{2}\)). Moreover, at each iteration, the difference \(\mathbf{d}^{(t-1)}-g\mathbf{h}_{j}\Delta x_{j|\omega}\) will require \(2(\frac{n+1}{2})(|\mathcal{A}|-1)\) arithmetic operations. If the conventional model was used, the corresponding complexity would be \(2n(|\mathcal{A}|-1)\). Then, and taking into account that computing \(\hat{\mathbf{y}}\) yields \(\mathcal{O}(\frac{2}{3}n^{3}+2n^{2})\), there is a gain in complexity if  
\[n(n+2)+t_{max}(n+1)(|\mathcal{A}|-1)+\frac{2}{3}n^{3}+2n^{2}<2 nt_{max}(| \mathcal{A}|-1)+2n^{2}, \tag{11}\]  
which holds true when  
\[t_{max}>\frac{\frac{2}{3}n^{2}+n+2}{(|\mathcal{A}|-1)(n-1)}\approx\frac{ \frac{2}{3}n^{2}+n}{|\mathcal{A}|-1}. \tag{12}\]  
Another possibility to keep complexity low, especially in higher constellations (64-QAM and higher), is to only sample one-symbol away neighbors, as suggested in [23].
# A Secure CDM-Based Data Analysis Platform (SCAP) in Multi-Centered Distributed Setting
## 3 Methods
### Communication between User and AS  
Before the communication between a user and the AS, it is assumed that the user has been appropriately registered in the AS. This assumption implies that the user is also enrolled in the SCAP. Figure 2 shows the process by which the AS authenticates the user of the SCAP platform. A user who wants to use the platform sends a message MC with the user's identifier \(ID_{C}\), such as an ID/password or biometric, and a signature \(sig_{M_{C}}\) of \(M_{C}\) to request proof of identity to the AS (flow 1 in Figure 2). The signature is transmitted along with the message to prevent tampering by attackers, whereas the message is transmitted  
Figure 1: Operating environment of SCAP.  
over the network to verify the sender. Because all components of the SCAP always transmit a message-signature pair, we omit the description of signatures for convenience in the remainder of this paper. If the requestor is a legitimate user, the AS returns a message \(M_{AS}\) including a cryptographically generated token, token, and its signature \(sig_{M_{AS}}\) to the user (flow 2 in Figure 2). This token is generated by encrypting the user's identity and expiration time using a symmetric key cryptographic algorithm, such as DES [34] or AES [35]. Therefore, only participants who know the key used to create the token can verify the validity of the token. The AS does not share the key used to generate the token with anyone to prevent a third party. In addition, for users not registered on the platform or illegal requests, the AS delivers an authentication failure response to the user. The user cannot use any SCAP service.
# Application of Three-Dimensional Porous Aerogel as Adsorbent for Removal of Textile Dyes from Water
## 2 Materials and Methods
### Preparation of Aqueous Solutions  
The pH values of the aqueous solutions were measured before and after each adsorption test. Before the experimental studies, the calibration of the pH meter \"Mettler Toledo Multi seven\" was carried out. Tests were performed at room temperature: in the temperature range of 20-23 \({}^{\circ}\)C.  
Glass measuring vessels of 50, 100 and 500 mL were used for this research. Before use, dishes were cleaned with distilled water and dried.  
Studies on the adsorption of the dyes using an adsorbent synthesized from paper and cardboard waste, aerogel, were carried out for up to 24 h by continuously stirring the aqueous solutions of the dye prepared with a magnetic stirrer. Based on the sources of the scientific literature, the following time intervals were chosen: 5 min, 10 min, 15 min, 20 min, 25 min, 30 min, 35 min, 40 min, 45 min, 50 min, 55 min, 60 min, 120 min, 240 min, 480 min and 1440 min.  
Aqueous solutions of the dyes with concentrations of 0.1 mg/L, 0.5 mg/L 1.0 mg/L, 2.0 mg/L, 5.0 mg/L, 10.0 mg/L, 100.0 mg/L and 200.0 mg/L were prepared for the adsorption tests.  
The required amounts of Congo red, Naphthol green B, Rhodamine B and Methylene blue dye powders were added to a 100 mL measuring flask, and distilled water was added to the 100 mL mark. The samples intended for the adsorption test under static conditions were transferred to larger-capacity (500 mL) glass vessels, placed in an automatic mixer and stirred at a speed of 400 revolutions per minute. Based on the results of research conducted by other scientists, it was decided to use a speed no higher than 400 revolutions per minute in order not to destroy the original structure of the aerogel capsule. At speeds greater than 400 rpm, the blades of the automatic mixer begin to break down the structure of the aerogel capsule by tearing apart the fibers that make up the adsorbent. During the tests, the aerogel capsules were completely immersed in the prepared aqueous dye solutions.  
It was decided to use a 0.8 g amount of aerogel under static conditions in the adsorption study.