esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In recent decades, the automotive industry has moved towards the development of advanced driver assistance systems to enhance the comfort, safety, and energy saving of road vehicles. The increasing connection and communication between vehicles (V2V) and infrastructure (V2I) enables further opportunities for their optimisation and allows for additional features. Among others, vehicle platooning is the coordinated control of a set of vehicles moving at a short distance, one behind the other, to minimise aerodynamic losses, and it represents a viable solution to reduce the energy consumption of freight transport. To achieve this aim, a new generation of adaptive cruise control is required, namely, cooperative adaptive cruise control (CACC). The present work aims to compare two CACC controllers applied to a platoon of heavy-duty electric trucks sharing the same linear spacing policy. A control technique based on reinforcement learning (RL) algorithm, with a deep deterministic policy gradient, and a classic linear quadratic control (LQC) are investigated. The comparative analysis of the two controllers evaluates the ability to track inter-vehicle distance and vehicle speed references during a standard driving cycle, the string stability, and the transient response when an unexpected obstacle occurs. Several performance indices (i.e., acceleration and jerk, battery state of charge, and energy consumption) are introduced as metrics to highlight the differences. By appropriately selecting the reward function of the RL algorithm, the analysed controllers achieve similar goals in terms of platoon dynamics, energy consumption, and string stability.  
platooning; CACC; energy consumption; vehicle dynamics; reinforcement learning; IA; LQC +
Footnote †: journal: Applied Sciences
## 2 The Proposed Method and Theoretical Background
### Multi-Level KRRD  
The raw vibration acceleration signal \(s(t)\) can be expressed by multiple residual signals as follows:  
\[s(t)=\sum_{i=1}^{n}r_{i}(t)=s_{\sum 1}(t) \tag{1}\]  
in which \(n\) represents the number of the residual signals, \(r_{i}(t)\) represents the \(i\)-th residual signal, and \(s_{\sum 1}(t)\) represents the set of the decomposed residual signals when the decomposition level is [1, \(n\)].  
When the decomposition level is [2, \(n\)], \(s_{\sum 2}(t)\) is described as follows:  
\[s_{\sum 2}(t)=s_{\sum 1}(t)-r_{1}(t) \tag{2}\]  
Therefore, the 1-th decomposed residual signal can be obtained by subtraction, and the corresponding expression is shown as follows:  
\[r_{1}(t)=K_{1}(s(t))=e^{-\frac{1}{2\lambda^{2}}(s(t)-\overline{s}(t))^{2}} \tag{3}\]  
Figure 1: The flowchart of the proposed method.  
in which \(K_{1}(\cdot)\) represents the non-parametric NW kernel estimation with the Gaussian kernel function. \(\lambda\) represents the bandwidth which can control the estimated smoothness, and it is taken as the reciprocal of the sampling frequency. \(\overline{s}(t)\) represents the Gaussian kernel function center, the same as the mean value of \(s(t)\).  
Similarly, the 2-th residual signal can be given as follows:  
\[r_{2}(t)=s_{\sum 2}(t)-s_{\sum 3}(t) \tag{4}\]  
Based on the above analysis, the \((n-1)\)-th residual signal can be obtained by the following equation. Meanwhile, an interference signal \(r_{n}(t)\) containing noise can be eliminated. To provide a more intuitive representation of the multi-level KRRD, the decomposition frame is shown in Figure 2.  
\[r_{n-1}(t)=s_{\sum n-1}(t)-s_{\sum n}(t) \tag{5}\]
# Novel Equalization Techniques for Space Division Multiplexing Based on Stokes Space Update Rule
## 3 Equalizers for SDM
### Multi-Mode 2M \(\times\) 2M Dynamic Equalization
#### 3.2.2 Stokes Space Algorithm  
The generalization of the SSA algorithm to \(2M\times 2M\) computes the error function into the Stokes space for each mode:  
\[f_{SSA,v}(h(n))=||\overline{S}_{v}(n)-\hat{\mathbf{S}}_{v}(n)||^{2}, \tag{16}\]  
where \(\overline{S}_{v}(n)\) and \(\hat{\mathbf{S}}_{v}(n)\) are the equalized and expected signal respectively of the mode \(v\) expressed in the Stokes domain. Moreover, the generalized update coefficients of the \(2M\times 2M\) SSA equalizer are:  
\[C_{1v}(n) =4\big{[}\overline{S}_{1v}(n)-\hat{S_{1v}}(n)\big{]}\overline{x}_ {v}(n)+4\Big{[}\big{(}\overline{S}_{2v}(n)-\hat{S_{2v}}(n)\big{)}+\sqrt{-1} \big{(}\overline{S}_{3v}(n)-\hat{S_{3v}}(n)\big{)}\Big{]}\overline{y}_{v}(n), \tag{17}\] \[C_{2v}(n) =4\Big{[}\big{(}\overline{S}_{2v}(n)-\hat{S_{2v}}(n)\big{)}- \sqrt{-1}\big{(}\overline{S}_{3v}(n)-\hat{S_{3v}}(n)\big{)}\Big{]}x_{v}(n)-4 \big{[}\overline{S}_{1v}(n)-\hat{S_{1v}}(n)\big{]}\overline{y}_{v}(n).\]
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 4 Results
### Error Analysis
#### 4.3.1 Feature Importance Ranking  
Figure 6 presents the ranked feature importance derived from the Enhanced ResNet model using permutation-based evaluation. Each bar represents the relative contribution of a specific input variable to the model's predictive accuracy. Among the 17 input features, Tree Cover, Slope, and Longitude emerged as the top three most influential variables, with importance scores of 0.050, 0.046, and 0.045, respectively. These features are strongly linked to vegetation structure and terrain, which aligns with the ecological drivers of aboveground biomass variation in mountainous regions like the Liuchong River Basin. The importance of EVI and B4 further highlights the model's reliance on spectral signals related to chlorophyll content and canopy reflectance in the red band.  
In particular, red-edge (B5-B7) and shortwave infrared (B11, B12) bands also showed notable importance, indicating their strong contribution to representing canopy structure, moisture content, and vegetation density. These bands are less prone to saturation and are well-known for their sensitivity to forest biophysical properties, which supports the model's capacity to capture structural heterogeneity in complex landscapes.  
Figure 5: Spatial distribution of estimated forest carbon stock (Mg/ha) across the Liuchong River Basin using different models: (**a**) Satellite, (**b**) Ground Truth, (**c**) Enhanced ResNet (Ours), (**d**) ViT, (**e**) GBDT, and (**f**) RF. Each row shows a different representative subregion at varying spatial scales.  
Interestingly, traditional vegetation indices such as NDVI and common spectral bands (e.g., B3, B7) showed relatively lower importance, suggesting that models incorporating more advanced architectures can shift reliance from conventional indices to more discriminative spatial-spectral cues. This result supports the design motivation of integrating channel attention mechanisms, which allow the network to prioritize informative bands and suppress redundant inputs. The balanced distribution of feature importance across topographic, spectral, and vegetation-related inputs also indicates that the model does not overfit to a narrow set of variables, improving its generalization in heterogeneous landscapes.
## 3 Results
### Optimization of Extraction Time  
The transfer of the analyte from aqueous samples to the organic solvent drop is critical in determining the extraction efficiency within a given timeframe. Consequently, extraction time is a key parameter that can significantly affect overall extraction efficiency. To assess the influence of extraction time on efficiency, a series of experiments were performed under consistent conditions, varying the duration from 5 to 90 min. The results, illustrated in Figure 5, showed that absorbance values increased with longer extraction times until stabilizing at 30 min (Figure 5). Therefore, an extraction time of 30 min was selected as optimal for subsequent experiments.  
Conditions: C\({}_{0}\): 2.5 \(\upmu\)g/L Hg(II); pH: 4.0, 2 mL; ionic liquid amount: 15 \(\upmu\)L HMIMPF6; extraction temperature: 26 \({}^{\circ}\)C; extraction speed: 750 rpm; added dodecanol amount: 80 \(\upmu\)L; final volume: 150 \(\upmu\)L.  
Figure 4: Effect of ionic liquid amount.  
Figure 5: Effect of extraction time.
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 6 Enablers for 6G and Relevant Technical Preparation and Implementation  
Many technologies, such as THz communication, holographic beamforming (HBF), mMIMO, AI, blockchain, and quantum communication (QC), are among the top technologies as 6G key enabling technologies. These technologies are discussed in the following subsection. Figure 5 introduces the most important 6G key enabling technologies.
## 2 Materials and Methods
### Interventions  
The participants were randomized into two groups: the TENS with PIR group (n = 25) and the TENS with SS group (n = 25). The duration of the interventions for both groups was 4 weeks, with sessions occurring 3 times per week, totaling 12 sessions. Each session lasted 50 min. Participants were requested not to take any analgesics or nonsteroidal anti-inflammatory drugs (NSAIDs) during the study period to avoid the potential analgesic effects of these medications.  
_TENS._ Each treatment session began with TENS, utilizing the Chattanooga Primera stimulator (DJO Consumer LLC, London, UK). The program selected was \"Pain Relief\" with the \"ENDORPHINIC\" setting, lasting 20 min, at a frequency of 5 Hz and a pulse width of 250 \(\upmu\)s [37; 38]. Two-channel electrodes were employed, and placed over the upper part of the trapezius muscle. The electrodes were positioned on both sides of the spine, 2-5 cm away from the spinal column. The distance between the lateral edges of the upper and lower electrodes was maintained at no less than 5 cm (Figure 2) [26].  
Figure 2: TENS electrode placement for neck pain.  
Static self-stretchingStatic neck muscle self-stretching exercises were supervised by experienced physiotherapists at the Physical Medicine and Rehabilitation Center and performed for 30 min following the TENS procedure. Patients in standing position were instructed to perform neck flexion, extension, lateral flexion, and rotation to the right and left, side bending combined with rotation to stretch the muscles in the cervical region until the onset of mild discomfort and holding each position for 30 s with a 15 s rest between stretches. The sequence of neck movements was repeated three times with a 10 s rest between repetitions [39; 40]. No additional aids were used, but to enhance the stretching, patients were asked to change the position of their arms during the movements. First, movements to the asymptomatic side were performed.  
Post-isometric relaxationIn this study, one of the autogenic inhibition techniques, post-isometric relaxation (PIR), also known as the \"contraction-relaxation\" technique, was employed. Depending on the targeted muscle group, the patient was seated, standing, or lying down, and instructed to exert resistance by pressing their head in the specified direction (at 50% of their maximum force) against the resistance provided by the therapist. During the pressing phase, resistance was sustained for 10 s, followed by a passive stretch (10 s duration) of the muscle in the opposite direction of movement. A total of five repetitions were conducted for each neck movement, with a 15 s rest interval between each repetition. All movements were executed without eliciting pain exceeding moderate intensity [41].  
All applied interventions were well tolerated by the study participants, and there were no dropouts.
# Biomethane Potential Test: Influence of Inoculum and the Digestion System
## 3 Results and Discussion
### Microbial Community Composition
#### 3.1.3 Effects of Pre-Incubation  
Based on the data of inocula I-WWP7 and I-WWP14, it is possible to determine the influence of pre-incubation on the microbial community composition. The ratio of the phylum _Firmicutes_ was enhanced after 14 days of pre-incubation. Compared to only 7 days of incubation, the ratio was 3.7% \(\pm\) 0.5% higher. One possible explanation for this is the higher degradation of complex material. However, the _Proteobacteria_ ratio decreased by 1.4% \(\pm\) 0.3% after 14 days compared to 7 days of pre- incubation. It was expected, that the higher amount of complex material would increase the _Proteobacteria_ population. [PERSON] et al. [47] found a similar behavior for the batch test. Additionally, there was no effect regarding the ratio of the phylum _Bacteroidetes_, which was expected to be lower when more complex material was used.  
The phyla _Cloacimonetes_, _Patescibacteria_, _Verrucomicobia_, and _Tenericutes_ showed an increased ratio when the pre-incubation time was longer. The lower abundance of _Cloacimonetes_ may be a result of fewer educts of acetogenesis. The functions of _Patescibacterium_, _Tenericutes_, and _Verrucomicobia_ are not yet described in the literature [41]. The increase of the phylum _Spirochaetes_ of 2.1% cannot be explained as its function is not yet known.  
The different incubation times did not show any effect on the population of the phylum _Actinobacteria_. During incubation, the ratio of the phylum _Chloroflexi_ showed a slow increase (0.9%).
## 2 Materials and Methods
### Model Experiments  
Model runs were conducted at an hourly time step for each of the two microforms, i.e., hummocks and hollows. Hourly measurements of I\({}_{\text{swr}}\), T\({}_{\text{air}}\), RH, and v\({}_{\text{w}}\) were used as meteorological drivers for the _DIMONA_ PB model for testing Hypotheses 1, 2, and 3. Gap-filling for the missing values was performed by using data from the Environment Canada weather station at Macdonald-Cartier Ottawa Airport, located \(\sim\)15 km away from the Mer Bleue bog [44]. Daily-observed d\({}_{\text{WT}}\) below the average hummock surface was adjusted by +25 cm below the average hollow surface to account for hummock-hollow elevation differences [26]. Then, daily-observed d\({}_{\text{WT}}\) was fragmented in the model into hourly values in hummocks and hollows, which were used as hydrological drivers to test Hypotheses 1 and 3. The daily near-surface \(\theta\) observed in hummocks at 10 cm depth and in hollows at 3 cm depth was fragmented in the model into hourly values used as hydrological drivers in each microform to test Hypotheses 2 and 3. Simulations during days with missing or low-quality observed d\({}_{\text{WT}}\) and/or near-surface \(\theta\) values were omitted from the model-data comparisons whenever possible. Model inputs of hourly I\({}_{\text{swr}}\), T\({}_{\text{air}}\), RH, and v\({}_{\text{w}}\), daily d\({}_{\text{WT}}\) and near-surface \(\theta\), physiological parameters for the shrubs and _Sphagntum_, and site and soil parameters were uploaded as.csv files in the corresponding online folders in _DIMONA_ (Figure 6b). Key parameters and their values used in this study for plant photosynthesis, productivity, and nutrient uptake are summarized in Table 1, while those for plant water control are given within the corresponding equations (Section Water Control on Plant Processes). The period of consecutive years with available hydrological and meteorological drivers and information on how many times that period should be repeated during simulations were entered online via the _DIMONA_ GUI (Figure 2a). The number of repetitions is chosen by the model user such that the modelled ecosystem could reach equilibrium, i.e., when ecosystem NPP equals litterfall [39]. Thus, the period 1998-2004 of available hydrological and meteorological drivers was repeated twice within each model run, driven either by observed d\({}_{\text{WT}}\) or near-surface \(\theta\), resulting in 14 years of simulations until ecosystem equilibrium was reached. Simulated ecosystem GPP in the last 5 years of simulations (i.e., the repeated 2000-2004 period) was used for testing Hypotheses 1, 2, and 3 by comparing model output vs. available EC-derived ecosystem GPP and other field observations.
# A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
## 2 Intra-Vehicle Security
### Challenges and Security Measures in Intra-Vehicle Networks
An additional layer of defence is often adopted to ensure that the malicious traffic produced by on-going attacks passing through the firewall is spotted and blocked: this is typically referred to as _Intrusion Detection Systems_ (IDSs). Intrusion Detection is a hot topic in intra-vehicle security: many recent works address the problem of implementing or adapting IDSs to intra-vehicle networks [26; 27]. In this category, researchers have proposed various solutions, including side-channel (CAN bus voltage)-based solutions [28], lightweight unsupervised IDSs [29], using statistical characteristics [30], privacy-oriented IDSs [31], Graph-oriented IDS [32], and Cosine-similarity-based IDS [33]. Once a threat has been detected, networks may adopt an _Intrusion Response System_ (IRS) to provide an adequate response to the threat. An example of its adoption in intra-vehicle networks is described in [34], wherein the authors propose a framework for IRS. A more thorough approach is adopted in [35], where the authors propose a _Security Operation Center_-based approach for real-time detection of incoming attacks using a vehicular Security information and event management (SIEM) platform. The proposed solution does not include intrusion detection or response capabilities directly, but it supports third-party IDSs via an intrusion detection manager component that takes care of providing interconnection between the IDS agents/probes and the IDS core. **Artificial Intelligence and Machine Learning**  
Among the detection strategies, the use of Artificial Intelligence and Machine Learning techniques has also seen increasing interest in the automotive domain. A recent survey on the topic can be found in [36]. Also, some notable uses of AI/ML techniques for intrusion detection are the following: ML for anomaly-based IDS [37] and temporal convolutional network IDS [38]. Discussions on AI and ML-based techniques are outside the scope of this paper, as they are part of a separate work to provide our solution with additional security services.
## 2 Numerical Approach
### The Numerical Model
 \tag{4}\]  
where \(\tau_{max}\) is the maximum stress of the shear spring of the interface element; \(\sigma_{n}\) refers to the stress of normal spring of the interface element; \(c\) and \(c_{r}\) represent the cohesion and the residual cohesion of the interface element in the tangential direction, respectively; \(\varphi\) is the friction angle of the interface element in the tangential direction; and \(f_{t}\) is the tensile strength of the normal spring of the interface element. Figure 8: Characteristics of the interface element. The constitutive relationship governing the behavior of the normal and shear spring is shown in Figure 8c and d, respectively. When the normal spring is compressed, the maximum stress of shear spring \(\tau_{max}\) increases continuously with the stress on the normal spring \(\sigma_{n}\). When the behavior of the normal spring is tensile, the maximum stress of the shear spring \(\tau_{max}\) is equal to the cohesion \(c\). If the stress reaches the tensile strength \(f_{t}\), the normal spring of the interface element will undergo tensile failure. The cohesion \(c\) is then replaced by the residual cohesion \(c_{r}\), and the tensile strength \(f_{t}\) is reduced to zero. The behavior of the shear spring of the interface element will return to its original state if the segment lining and secondary lining overlap and the normal spring generates pressure again. Based on these deformation properties, this refined numerical calculation model can not only reflect the shear behavior, but also realistically simulate the separation and subsequent overlap between segment lining and secondary lining. The constitutive relationship governing the behavior of the normal and shear spring can be expressed thus:  
\[\sigma_{n}=k_{n}u_{n}\quad(u_{n0}\leq u_{n}) \tag{5}\]  
\[\tau=\quad \begin{cases}k_{s}|u_{s}|&(0\leq u_{s}<|u_{s0}|)\\ \tau_{\max}&(|u_{s0}|<u_{s})\end{cases}  \tag{6}\]  
where \(\tau\) is the stress of the shear spring of the interface element; \(|u_{s}|\) is the absolute strain in the shear spring of the interface element; \(|u_{s0}|\) is the strain in the shear spring corresponding to the maximum shear stress \(\tau_{max}\); \(u_{n}\) denotes the strain in the normal spring of the interface element; \(u_{n0}\) is the strain in the normal spring corresponding to the tensile strength \(f_{t}\); and \(k_{s}\) and \(k_{n}\) represent the stiffness of the shear and normal spring of the interface element, respectively. The stiffness of the interface element in a circular tunnel can be calculated empirically using Equation (7), as suggested in the literature [43; 45]:  
\[k_{n}=k_{s}=100\text{max}\left[\frac{\left(K+\frac{4}{3}G\right)}{\Delta z_{ \text{min}}}\right] \tag{7}\]  
where \(K_{z}\) and \(G_{z}\) represent the bulk and shear modulus of adjoining zone, respectively; \(\Delta z_{\text{min}}\) is the smallest dimension of an adjoining zone in the normal direction. Parameters such as tensile strength \(f_{t}\), cohesion \(c\) and \(c_{r}\), and friction \(\varphi\) were chosen from the empirical values given in the literature, as summarized in Table 4.