esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Meta-Analysis Results  
Preliminary analysis showed that the sample sizes in different studies were significantly different. Some studies had a large sample size of more than 3000 participants, e.g., [22; 23], while others had only 20 participants, e.g., [24]. Additionally, some studies used surveys, while others employed experiments. To address the methodological differences and disparity in participant sizes across the studies, only the percentage of occurrences of NDRAs from each study was selected and averaged. This meta-analysis also distinguishes between different levels of automation. For studies that reported results across multiple automation levels, we included the data for each level separately. However, some studies combined automation levels (e.g., L3 & L4, L4 & L5). In such cases, we analyzed them as two distinct categories: L3 & L4 and L4 & L5. Table 4 presents the activities in each category along with their occurrence percentages. Figure 2 shows the mean occurrence percentages of different NDRAs. The values increase from L3 (27% \(\pm\) 17%) to L4 (41% \(\pm\) 20%) and then decrease slightly at L5 (39% \(\pm\) 15%). For group activities, the percentages are 33% \(\pm\) 23%, 52% \(\pm\) 8%, and 56% \(\pm\) 10% for L3, L4, and L5, respectively. For personal activities, the percentages are 26% \(\pm\) 16% for L3, 40% \(\pm\) 21% for L4, and 38% \(\pm\) 14% for L5. However, when L3 and L4 are combined, the mean occurrence percentage decreases to 27% \(\pm\) 26%. Similarly, combining L4 and L5 results in a lower mean occurrence percentage of 28% \(\pm\) 19%. This suggests that merging these levels in the study may reduce the observed mean values. Figure 3 provides more details on personal activities. The mean percentages of different categories are 28% \(\pm\) 7%, 38% \(\pm\) 11%, and 36% \(\pm\) 7% for L3, L4, and L5, respectively. While the results follow a similar trend to the occurrence percentages, it is notable that in L4, the variation in activities is the highest, and in L5, personal activities increase. It is worth mentioning that \"Vehicle Monitoring Activities\" and \"Rest and Relax\" decrease from L4 to L5. However, within the \"Rest and Relax\" category, \"Rest and Relax--Looking at scenery\" drops significantly at L5, whereas \"Rest and Relax--Sleeping\" continues to rise, as shown in Table 4.  
Figure 2: The mean occurrence percentages and standard deviation of different NDRAs regarding automation levels.
# LoRaWAN Path Loss Measurements in an Urban Scenario Including Environmental Effects
## 4 Experimental Setup
### Sensors  
Each EN includes a set of sensors that captures weather variations. A brief description of each sensor is provided as follows.  
* An Aosong DHT22 sensor for temperature (accuracy: \(\pm\)0.5 \({}^{\circ}\)C) and relative humidity (accuracy: \(\pm\)2%). Regarding temperature, this sensor includes a transistor-based transductor. Concerning relative humidity, it consists of a capacitive sensor. The sensor contains a one-wire communication protocol to send the current values to the LoPy4 microcontroller, lowering the errors in the analog-to-digital conversion.
* A Bosch BMP280 sensor for barometric pressure (accuracy: \(\pm\)1 hPa). This sensor operates in a range of 300-1100 hPa, which is suitable for use in Medellin, Colombia because the barometric pressure is up to 900 hPa. This sensor embeds an I2C communication to communicate with the microcontroller, lowering the errors in analog-to-digital conversion.
* A Honeywell HPMA11550 sensor for particulate matter PM2.5 (accuracy: \(\pm\)15%). Its operation is based on laser scattering, which detects and counts particles with concentrations up to 1000 \(\mathrm{\SIUnitSymbolMicro m}^{3}\). The sensor communications are based on the RS232 protocol. Again, this communication reduces the errors in analog-to-digital conversion.
* In addition, we included a Texas Instruments INA219 energy sensor (accuracy \(\pm\)0.5%) in the printed circuit board to quantify the consumption under different radio configurations. It is based on a shunt resistor that can monitor voltages up to 26 VDC and currents up to 5 A. The sensor sends the information to the microcontroller by using the I2C protocol.
* We added a Stevenson screen to protect the sensors from rainy conditions without losing accuracy and avoiding possible sensor saturations.  
We also designed and built the ENs' testbeds as shown in Figure 7. The electronics are inside a IP65 box to protect it from rain and condensation caused by high relative humidity.
# SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion
## 2 Related Works
### KG Embedding with Ontology  
At present, many scholars are studying KG embedding with ontology. Semantically smooth embedding for knowledge graphs (SSE) [26] models the intrinsic geometry of KG based on the assumption that entities belonging to the same semantic category are close to each other in the embedding space (semantically smooth). SSE [26] uses two manifold learning algorithms, Laplacian Eigenmap and Local Linear Embedding, as regularization terms to model the smoothness hypothesis. Differentiating concepts and instances for knowledge graph embedding (TransC) [27] models concept embeddings as a sphere and assumes that the embedding vectors corresponding to instances of categories belonging to concepts should lie in this sphere. TKRL [17] focuses on the Type hierarchy of KG and believes that entities have different representations under different categories. And use the layered type as a mapping matrix and use type encoders to design. A knowledge-driven representation learning method with ontology information constraints (TransO) [28] model considers the limitations of the three types of ontology information, type information, relation constraint information, and hierarchical structure information, and maps entities and relationships to the ontology information-limited space. Based on the TransE model, loss functions are defined in the basic space and ontology information-limited space and combine the two for representation learning. Knowledge graph embedding with hierarchical relation structure (HRS) [29] learns representations of Relation clusters, Relations, and Sub-relations separately. It sums the three as the embedding vector of the relationship, thus modeling the hierarchical structure of the relationship. A representation learning method for knowledge graphs with relation hierarchical structure (TransRHS) [30] builds on HRS [29]. It encodes each relation as a vector and relation-specific spheres in the same space. TransRHS [30] uses the relative positions between vectors and spheres to model sub-relationships, which embodies the inherent generalization relationship between relations. The Universal representation learning of knowledge bases by jointly embedding instances and ontological concepts (JOE) [31] model proposes a method that considers that KG and ontology use different embedding spaces and enables cross-space interaction between the two embeddings. A Modified Joint Knowledge Graph Embedding Model for Concepts and Instances (JECI++) [32] simplifies hierarchical concepts and links instances to them, making identifying cases based on neighboring instances and simplified concepts easier. It uses circular convolution to locate instances in the embedding space and employs CBOW and Skip-Gram strategies to embed simplified concepts and instances jointly.
# Federated Learning for Secure In-Vehicle Communication
## 3 Federated Learning-Based Intrusion Detection Framework
### Proposed Architecture  
We consider a vehicular network where a set of vehicles operates within a specific geographical area (e.g., a city) and is connected to the same edge server. The set of vehicles, along with the edge server, constitutes a federated learning environment where the edge server is the FL server, and the vehicles are the federated learning clients and are responsible for locally training the machine learning model defined and initialized by the FL server. Each vehicle continuously collects CAN bus data and processes it using its onboard computing resources (i.e., OBU). The vehicle's local computing entity preprocesses the data and uses it to locally train a model that has been initialized by the edge server. Once local training is completed, each vehicle sends updated model weights to the edge server via RSUs using WiFi or LTE, reducing direct cloud dependencies. The model consists of multiple layers, where the final layer is responsible for attack classification. To ensure privacy, vehicles do not transmit the last classification layer when sending updates to the edge server. The edge server acts as an aggregator; it receives the model updates from the vehicles, performs an aggregation, and sends the updated weights back to the vehicles. Figure 3 depicts the reference architecture of our framework.
## 3 Methodology and Experimental Set-Up
### Experiment 1 Setup: Recall/Detection Rate Metric  
We have used seven URI attack datasets to assess the Recall/Detection Rate metric. Some datasets are public (e.g., [46]) while others are in-house datasets generated by the authors in previous works (e.g., [21, 47, 48]) such as _A-420_ that includes critical attacks with CVEs from 2017 and 2018, or _ataques-800_ and _fuwf-2200_ that include attacks generated from Snort's ruleset according to the CVE labeling. Table 2 summarizes the main characteristics of these datasets. Since all attacks were recorded at least two years before the signatures used in our experiment, we can consider that most attacks should be known to the SIDS.  
Figure 2 illustrates the experiment carried out to ascertain the Recall/Detection Rate of the systems and configurations shown in Table 1. The alarms generated by each system have been analyzed both individually (for SIDS comparison) and combined with an ensemble module that performs three different decisions: union (i.e., at least one SIDS detects attack), intersection (i.e., all SIDS detect attack) and voting majority (at least two SIDS detect attack). Since each register in the attack datasets represents an attack instance, those registers that do not trigger an alarm will be considered as False Negative (_FN_). Therefore, the recall metric can be calculated and used to evaluate the detection capability of the systems under study. The cooperative decision made by the ensemble module will also let us figure out whether an ensemble decision improves the detection performance.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**SIDS** & **Detection Sensitivity (Configuration)** & **Base Rule Set** \\ \hline InspectorLog [18] & low. (default recommended rules) & \\ (Snort rules) & med. (all rules except DELETED) & Talos (2021) \\ \hline ModSecurity [38] & low. (PL1) & \\ med. (PL2) & OWASP CRS 3.2.0 (2019) \\  & max. (PL4) & \\ \hline Nemesida WAF [45] & default (Free version) & proprietary (September-2021) \\ \hline \hline \end{tabular}
\end{table}
Table 1: SIDS and configurations under study.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **\#URIs** & **Type (Source)** & **Content** & **Remarks** \\ \hline Fwaf-bad & 48216 & public [46] & attacks (\(<\)2017) & WAF attacks \\ \hline Fwaf-2200 & 2 200 & public [46] & attacks (\(<\)2017) & Fwaf-bad’s subset for CVEs covered by rules \\ \hline ataques-800 & 832 & in-house [47] & attacks (\(<\)2017) & type-3 CVE included in Snort’s signatures \\ \hline ataques-1100 & 1176 & in-house [47] & attacks (\(\leq\)2017) & types 1 ( CVE 2016 and 2017), 3 and 4 \\ \hline rdb & 934 & in-house [21] & attacks (\(<\)2009) & attacks generated from RDB \\ \hline osvdb & 6897 & in-house [48] & attacks (\(<\)2009) & attacks generated from OSVDB \\ \hline A-420 & 420 & in-house & attacks (2017/18) & attacks generated with CVE 2017-18 and CVSS\textgreater{}9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Attack datasets used.
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 4 DMS Architecture and Functional Overview
### Data Processing
#### 4.2.4 AI Component  
The AI-powered module is responsible for continuously assessing driver alertness using the Mediapipe face model. It processes real-time visual input, specifically facial landmarks, head orientation, gaze direction, and eye opening to determine cognitive states such as focus, distraction, or drowsiness [30]. Pre-processed video frames from the in-cabin camera serve as input to the AI model, which applies machine learning algorithms to classify the driver's state. The model's outputs inform higher-level safety decisions by indicating whether the driver is alert, distracted, or potentially incapacitated [7].
## 3 Experiments and Results
### Ablation Experiment  
Lastly, to verify the effectiveness of the improvements made in this paper for the task of water pump fault detection, a module ablation experiment was conducted. The CNN module, the transformer module, and BiGRU module were removed, respectively, and then the training and testing were carried out. These results are presented in Table 8.
# Performance of Adaptive Bit-Interleaved Polar Coded Modulation in FSOC System
## 5 Conclusions  
This work employs polar spectrum and average PEP as analytical tools to investigate the upper bound of the error probability, enabling the proposition of an adaptive bit-interleaved polar coding modulation (A-BIPCM) approach. The theoretical model demonstrates that, within the simulated atmospheric channel established using the Monte Carlo model, this coding method effectively mitigates the impact of attenuated atmospheric conditions, as evidenced by its superior performance in terms of BER. Furthermore, an experimental Free Space Optical Communication (FSOC) setup incorporating a controllable atmospheric turbulence channel was implemented. When T = 160, when the optical power of the detector is \(-\)16 dBm, traditional polar code encoding and decoding BER = \(9.13\times 10^{-4}\), A-BIPCM scheme BER = \(7.02\times 10^{-5}\). A comparison between the A-BIPCM method and the polar code encoding and decoding scheme without adaptive bit-interleaving revealed a one-order-of-magnitude improvement in BER performance for the former, consistent with the simulation results. This substantiates that the proposed A-BIPCM method possesses enhanced error correction capabilities, lower computational complexity, and effectively addresses the issue of a persistent long string of bit errors caused by turbulence.  
Conceptualization, Q.J. and Z.L. ([PERSON]); methodology, Q.J.; software, Q.J.; validation, H.Y. and Q.J.; formal analysis, Q.J.; investigation, Z.L. ([PERSON]), C.C., H.L. and G.J.; resources, X.Z.; data curation, Q.J.; writing--original draft preparation, Q.J.; writing--review and editing, Z.L. ([PERSON]); visualization, S.L.; supervision, Z.L. ([PERSON]) and P.L.; project administration, Z.L. ([PERSON]); funding acquisition, Z.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China Ye Qisun Science Foundation, grant number U2141231. China Ye Qisun Science Foundation totals 2.6 million.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
Conflicts of Interest: The authors declare no conflicts of interest.
## 2 Spectral Components of Brushed DC Motor Current  
The voltage and current of brushed dc motors have two different spectral components: dc and ac (alternating current). The dc component is the supply power. The ac component is mainly produced by the back-EMF, which is induced in the coils. The back-EMF is a voltage signal added to the voltage of the dc motor.  
Figure 1 represents a real brushed dc motor and Thevenin's equivalent of a power supply that supplies the motor. The power supply is modeled according to [PERSON]'s equivalent circuit with an ideal source and series resistance, where \(\mathrm{V_{cc}}\) is the ideal voltage source and \(\mathrm{R_{s}}\) is the series resistance. From this, the relation between voltage \(\mathrm{V_{m}}\) and current \(\mathrm{I_{m}}\), of a brushed dc motor is given by:  
\[\mathrm{I_{m}}=(\mathrm{V_{cc}}-\mathrm{V_{m}})/\mathrm{R_{s}} \tag{1}\]  
Two observations can be done according to Equation (1). The first is that because \(\mathrm{V_{cc}}\) and \(\mathrm{R_{s}}\) are constants, the current and voltage of the motor will be related to each other, and every component in the voltage will also be present in the current. The second is that \(\mathrm{R_{s}}\) is usually small, and \(\mathrm{V_{cc}}\) is similar to \(\mathrm{V_{m}}\). Therefore, the magnitude of the current spectral components compared to that of the current dc component will be larger than the magnitude of the voltage spectral components compared to that of the voltage dc component. Consequently, measuring the current spectral components will be easier than measuring the voltage spectral components. Then, spectral components of the current are usually employed for sensorless speed detection of brushed dc motors, instead of employing the spectral components of the voltage.  
The nature of the spectral components will be described in the next two subsections.
# Recent Advances in the Targeting of Head and Neck Cancer Stem Cells
## 6 Therapeutic Targeting of CSCs
### Targeting Receptors  
A drug approved by the FDA, also used for targeting CSC markers, is cetuximab which targets the EGFR. EGFR enhances the stemness and advancement of oral cancer through the inhibition of autophagic degradation of Sox2 [158].  
Several studies are reporting the antitumor activity of cetuximab on HNSCC and other cancers [159; 160; 161]. [PERSON] et al. recently demonstrated that combined therapy with EGFR/Notch bispecific antibodies and PARP inhibitor decreases the subpopulation of stem-like cells, reduces the frequency of tumor-initiating cells, and delays tumor recurrence after irradiation in a lung cancer model [162]. However, as a recent review article pointed out that Notch signaling pathway is critical in HNSCC, this strategy could be tested in this type of cancer as well [155]. One recent study tested the therapeutic combination of cisplatin, cetuximab, and valpro acid (an antiepileptic drug and a histone deacetylase inhibitor) and showed synergistic antiproliferative and pro-apoptotic effects on four different 3D-self-assembled spheroid models. Also, the combined treatment completely obstructed HNSCC xenograft tumor growth in nude mice [163]. Another study was focused on determining how EGFR inhibition affects different subtypes of cancer stem cells, i.e., cancer stem cells shifting from epithelial to mesenchymal states. They discovered that the inhibition of EGFR reduces cell proliferation but without significant cell death induction. Additionally, cetuximab and erlotinib induced cellular differentiation, which typically means that the cells should be more susceptible to chemotherapy or irradiation therapy [160]. [PERSON] et al. showed that dual monoclonal antibody HER family blockade (etuximab and perutzumab) combined with photon irradiation is highly effective in the inhibition of growth of HNSCC CSCs through the blocking of downstream AKT-mTOR and Ras-MAPK signaling [164].  
Another receptor tyrosine kinase pan-inhibitor, ponatinib, has been shown to eliminate HNSCC CSCs [165]. Also, an interesting study by [PERSON] et al. showed there is a difference in factors secreted by CAFs and normal fibroblasts, and these factors were connected with EGFR, Insulin-like growth factor (IGFR), and Platelet-derived growth factor receptor (PDGFR). So, when the authors applied different inhibitors of these receptors, they showed reduced sphere formation and anchorage-independent growth [166]. Also, afatinib, a pan-EGFR inhibitor, reduced tumorigenicity and radiosentisized HNSCC cells by specifically targeting CSCs [167].
###### Abstract  
The training system Semi-Minimal-Pruned Hedge (SMPH) blends features of traditional Vertical Shoot Positioning-type (VSP) trellising systems with the concept of minimal pruning. While saving labor, this training system results in relatively high crop load and a poor leaf area to fruit weight-ratio (LFR), and thus, needs to be able to ripen grapes in a cool to moderate climate. For these reasons the impact of yield regulation strategies, including (i) shoot thinning (Darwin-Rotor), (ii) biotechnological thinning (Gibberellic acid), and (iii) bunch thinning (harvest machine) were trialed in a three year study at Geisenheim, Germany between 2017 and 2019 using Riesling (_Vitis vinifera_ L.). The average yield per vine in SMPH (5.34 \(\pm\) 1.10 kg) was 61.1% higher with a narrower LFR (14.01 cm\({}^{2}\) g\({}^{-1}\)), compared with VSP (3.32 \(\pm\) 1.02 kg, LFR: 16.99 cm\({}^{2}\) g\({}^{-1}\)). The yield was successfully reduced and LFR simultaneously increased with shoot thinning (\(-\)33.1%, LFR: 19.04 cm\({}^{2}\) g\({}^{-1}\)), biotechnological thinning (\(-\)18.3%, LFR: 16.69 cm\({}^{2}\) g\({}^{-1}\)) and bunch thinning (\(-\)37.3%, LFR: 21.49 cm\({}^{2}\) g\({}^{-1}\)). Ripening was delayed in SMPH. On average, two maturity thresholds (14.1 \({}^{\circ}\)Brix and 18.2 \({}^{\circ}\)Brix) were achieved 129 GDD (seven days according to the recorded daily mean temperatures, respectively) and 269 GDD (16 days) later in non-thinned SMPH, compared to VSP. All thinning treatments accelerated maturity progress ranging from 27 GDD (two days) to 58 GDD (three days) for 14.1 \({}^{\circ}\)Brix and 59 GDD (three days) to 105 GDD (six days) for 18.2 \({}^{\circ}\)Brix. Apart from immediate benefits on the economic efficiency, the adaption of the leaf area to fruit weight ratio using SMPH holds high potential to, (i) produce grapes targeting specific wine profiles and/or (ii) reducing the velocity of ripening under conditions of climatic change.  
SMPH; training systems; climate change; crop thinning; maturity progress; maturity delay; velocity of ripening; primary fruit components +
Footnote †: journal: Applied Sci.