esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Halo Parameter  
The halo parameter \(H\) generalizes the spatial-profile kurtosis \(h\) to the 2D phase-space \((x,x^{\prime})\) using the kinematic invariants of the particle distribution, which is given as [23]:  
\[\begin{split} H&=\frac{\sqrt{3I_{4}}}{2I_{2}}-2,\\ I_{2}&=\left\langle x^{2}\right\rangle\left\langle x ^{\prime 2}\right\rangle-\left\langle xx^{\prime}\right\rangle^{2},\\ I_{4}&=\left\langle x^{4}\right\rangle\left\langle x ^{\prime 4}\right\rangle+3\left\langle x^{2}x^{\prime 2}\right\rangle^{2}-4 \left\langle xx^{\prime 3}\right\rangle\left\langle x^{3}x^{\prime}\right\rangle, \end{split} \tag{1}\]  
where \(\left\langle x^{m}x^{\prime m}\right\rangle=\frac{1}{N}\sum_{i=1}^{N}(x_{i}- \bar{x})^{m}\left(x_{i}^{\prime}-\bar{x}^{\prime}\right)^{n}\), \(\bar{x}\) and \(\bar{x}^{\prime}\) are the mean values of \(x\) and \(x^{\prime}\), respectively. One can see that \(I_{2}\) factor is exactly the product of the trace-space beam emittance.  
In the linear focusing system with a uniform focusing strength of \(K\), where \(x^{\prime\prime}=-K^{2}x\), the halo parameter \(H\) is an invariant, since  
\[\frac{\mathrm{d}H}{\mathrm{d}z}=\frac{3}{4I_{2}\sqrt{I_{4}}}\frac{\mathrm{d} I_{4}}{\mathrm{d}z}-\frac{\sqrt{3I_{4}}}{2I_{2}^{2}}\frac{\mathrm{d}I_{2}}{ \mathrm{d}z}=0, \tag{2}\]  
where the kinematic invariants \(I_{2}\) and \(I_{4}\) are positive figures with derivations of  
\[\begin{split}\frac{\mathrm{d}I_{2}}{\mathrm{d}z}=\frac{\mathrm{d }e_{x}^{2}}{\mathrm{d}z}=&\frac{\mathrm{d}e_{x}^{2}}{\mathrm{d}z }=&\frac{\mathrm{d}}{\mathrm{d}z}\left(\left\langle x^{2}\right \rangle\left\langle x^{\prime 2}\right\rangle-\left\langle xx^{\prime}\right\rangle^{2}\right)\\ =& 2\left\langle xx^{\prime}\right\rangle\left\langle x ^{\prime 2}\right\rangle+2\left\langle x^{2}\right\rangle\left\langle x^{ \prime}x^{\prime\prime}\right\rangle\\ &-2\left\langle xx^{\prime}\right\rangle\left\langle x^{\prime 2 }\right\rangle-2\left\langle xx^{\prime}\right\rangle\left\langle xx^{ \prime\prime}\right\rangle\\ =& 0,\end{split} \tag{3}\]and  
\[\begin{split}\frac{\mathrm{d}I_{4}}{\mathrm{d}z}=& 4\Big{\langle}x^{3}x^{\prime}\Big{\rangle}\Big{\langle}x^{ \prime 4}\Big{\rangle}+4\Big{\langle}x^{4}\Big{\rangle}\Big{\langle}x^{ \prime 3}x^{\prime\prime}\Big{\rangle}\\ &+12\Big{\langle}x^{2}x^{\prime 2}\Big{\rangle}\Big{\langle}xx^{ \prime 3}\Big{\rangle}+12\Big{\langle}x^{2}x^{\prime 2}\Big{\rangle}\Big{\langle}x^{2}x^{ \prime}x^{\prime\prime}\Big{\rangle}\\ &-4\Big{\langle}x^{\prime 4}\Big{\rangle}\Big{\langle}x^{3}x^{ \prime}\Big{\rangle}-4\Big{\langle}xx^{\prime 3}\Big{\rangle}\Big{\langle}x^{3}x^{ \prime\prime}\Big{\rangle}\\ &-12\Big{\langle}xx^{\prime 2}x^{\prime\prime}\Big{\rangle} \Big{\langle}x^{3}x^{\prime}\Big{\rangle}-12\Big{\langle}xx^{\prime 3}\Big{\rangle} \Big{\langle}x^{2}x^{\prime 2}\Big{\rangle}\\ =& 0.\end{split} \tag{10}\]
# A Comparison of Pooling Methods for Convolutional Neural Networks
## 2 Popular Pooling Methods
### Average Pooling Method  
The input is segmented into rectangular pooling areas, and an average pooling layer down samples by calculating the average values of each region. The implementation of the proposed idea was performed on the first convolution-based deep neural network [25]. Figure 3 provides a visual representation to clarify how the average pooling operation functions. In the average pooling strategy, the image input is divided into a number of separate rectangular boxes. The average of the values in each rectangle's box is calculated and the output channel is presented. Average pooling is expressed mathematically as:  
\[f_{ave}(X)=\frac{1}{N}\sum_{i}^{N}=1x_{i} \tag{3}\]  
where \(x\) is a vector representing activations from a rectangular box of \(N\) permutations in an image or a channel (for example, the size of the rectangular area in Figure 3). The shortcoming seems to be mostly that this leads to a decline of information in terms of contrast. All of the activation values in the rectangular box are considered when estimating the mean. The estimated mean will indeed be low if the strength of all the activation functions is low, resulting in diminished contrast. The problem will worsen once most of the activations in the pooling zone have a zero value. In that situation, the convolutional feature characteristic would be reduced significantly [26].  
Since neither the approaches of max nor average pooling consistently performs better than the other, different methods have been proposed that combine the advantages of max pooling and average pooling. In this research area, max pooling and average pooling are directly combined with weights and soft pooling. These algorithms add more parameters than max average pooling, which requires more time to learn or tune the parameters, and computational overhead increases [27].
###### Abstract  
We report a novel method to fabricate lithium-ion batteries cathodes with the NH\({}_{4}\)F pretreatment. In this study, NH\({}_{4}\)F-pretreated Li\({}_{1.25}\)Ni\({}_{\bf 0.20}\)Fe\({}_{0.13}\)Co\({}_{0.33}\)Mn\({}_{0.33}\)O\({}_{2}\) hollow nano-micro hierarchical microspheres were synthesized for use as cathode materials. The X-ray diffraction patterns of NH\({}_{4}\)F-pretreated Li\({}_{1.25}\)Ni\({}_{\bf 0.20}\)Co\({}_{0.33}\)Fe\({}_{0.13}\)Mn\({}_{0.33}\)O\({}_{2}\) were analyzed with the RIETAN-FP software program, and the results showed that the samples possess a layered \(\alpha\)-NaFeO\({}_{2}\) structure. The effects of pretreatment with NH\({}_{4}\)F on the electrochemical performance of the pristine material were evaluated through charge/discharge cycling, the rate performance, and electrochemical impedance spectroscopy (EIS). Pretreatment with NH\({}_{4}\)F significantly improved the discharge capacities and coulombic efficiencies of Li\({}_{1.25}\)Ni\({}_{\bf 0.20}\)Co\({}_{0.33}\)Fe\({}_{0.13}\)Mn\({}_{0.33}\)O\({}_{2}\) in the first cycle and during subsequent electrochemical cycling. The sample pretreated with an appropriate amount of NH\({}_{4}\)F (NFCM 90) showed the highest discharge capacity (209.1 mA h g\({}^{-1}\)) and capacity retention (85.2% for 50 cycles at 0.1 C). The EIS results showed that the resistance of the NFCM 90 sample (76.32 \(\Omega\)) is lower than that of the pristine one (206.2 \(\Omega\)).  
lithium-ion batteries; cathode material; microspheres; NH\({}_{4}\)F pretreatment; spinel phase; electrochemical performance
## 3 Proposed ESM Block-DNN Detector
### B-DNN Parameters and Training
The input vectors for training are derived from the channel matrix \(\mathbf{H}\) as well as the received signal vector \(\mathbf{y}\), which could be formulated as \(\mathbf{d}_{i}=[\mathbf{f}_{SFVG}(\mathbf{y}_{j})^{T},\mathbf{f}_{SFVG}( \mathbf{H}_{i}^{\prime})^{T}]^{T}\). For the tags, a one-hot encoding scheme is implemented, creating a vector of length M that corresponds to the transmitted symbol. This vector represents the M-PSK symbol constellation, thereby providing a distinct label for each point in the constellation. At the same time, during the training phase, certain issues often arise, such as overfitting. To mitigate the impact of this problem, we implement several strategies. Firstly, we employ L2 regularization, which imposes a penalty on the sum of the squares of the weights, preventing them from becoming excessively large. This helps to reduce the complexity of the model and prevent overfitting. The regularization coefficient is set to 0.001 during the training phase. Additionally, batch normalization is incorporated after the hidden layers; this technique normalizes the features of each mini-batch to have a mean of zero and a variance of one. Furthermore, dropout is introduced at the end of the hidden layers, which randomly sets a proportion of neurons and their connections to zero with a certain probability. This prevents neurons within the network from becoming overly reliant on specific features, thereby encouraging the network to learn more robust and generalized feature representations. These strategies can significantly reduce the effects of overfitting.
## 6 Conclusions  
In this paper, UFMC, as a new 5G waveform, and UFMC-CDMA were proposed as a RadCom system, and they proved to be suitable for this application by means of simulations while offering great radar and communication performance. First, the OFDM and UFMC waveform parametrization and the UFMC-CDMA signal model details were described. An analytical model of the UFMC BER was also provided. On the basis of RadCom system requirements, multiaccess techniques can be implemented to support a V2X multiuser environment; consequently, simulations were performed with and without multiaccess. The UFMC waveform is capable of supporting a RadCom system with affordable complexity, similar to OFDM, while offering optimal spectral efficiency. In addition, filter length was investigated, and filter length should be chosen to be smaller than \(\frac{N_{c}}{Q}\) for communication, Regarding radar requirements, it should be chosen to be proportional to \(\frac{N_{c}}{Q}\). As for UFMC-CDMA, it was proven that the choice of spreading sequence can affect the overall performance of the radar. The more scrambled the sequence is, the more optimal results are. Furthermore, filter-frequency selectivity affected UFMC-CDMA performance. The performed simulations did not hold any error-correction coding; thus, added error correction coding would enhance system performance.  
Figure 11: Filter length evaluation over AWGN channel.  
**Author Contributions:** conceptualisation, I.K., K.Z., R.E., F.E., and N.I.; methodology, I.K., K.Z., R.E., and F.E.; software, I.K. and K.Z.; validation, R.E., F.E., and N.I.; formal analysis, R.E. and F.E.; investigation, I.K., K.Z., R.E., and F.E.; resources, R.E. and F.E.; writing--original-draft preparation, I.K. and K.Z.; writing--review and editing, I.K. K.Z., R.E., and F.E.; supervision, R.E., F.E., and N.I.; project administration, R.E. and F.E.; funding acquisition, R.E. and F.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The present research work was supported by the European project SECREDAS funded ECSEL.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Studies
### Traditional Methods for Variance Adjustment in VarDA Systems
#### 2.1.2 Adaptive Variance Updating  
[PERSON] et al. (2005) developed a diagnostic-based variance tuning method, iteratively adjusting **B** and **R** based on statistical consistency relations in an observation space [30]. Applied to the French ARPEGE 4D-Var system, this method identified the systematic overestimation of background and observation errors, improving variance estimates and capturing observation error correlations. However, its effectiveness hinges on the availability of high-density, high-quality observations and assumes independence between background and observation errors, limiting robustness in data-sparse regions. Furthermore, the method lacks flow-dependent and temporally adaptive covariance adjustments, restricting its operational applicability.  
To enhance adaptability, [PERSON] et al. (2019) proposed two iterative methods, Covariance Updating iTerativE (CUTE) and Partially Updating Best Linear Unbiased Estimator(PUB) [31]. These approaches iteratively refine **B** by incorporating evolving background-observation error covariances, introducing a scaling factor (\(\alpha\)) to regulate variance trace and prevent excessive reduction over iterations. CUTE directly updates **B** based on background-observation error covariance, while PUB extends the assimilation space by jointly optimizing **B** and **R**. Despite improving error correlation estimation and assimilation accuracy, these methods contradict the independence assumption of **B** in variational assimilation, potentially resulting in non-convergence. PUB incurs higher computational costs, and its optimization impact on assimilation performance is less pronounced than that of CUTE. Both methods rely on empirically tuned scaling factors, limiting their full adaptability in high-dimensional applications.  
Hybrid data assimilation methods offer an alternative by combining ensemble-derived and static background error covariances, introducing flow dependency while maintaining statistical robustness [17; 32]. By weighting these components, hybrid approaches indirectly adapt background error variances as ensemble covariances naturally evolve over time. However, these methods demand substantial computational resources due to the need for ensemble forecasts. Additionally, [PERSON] et al. (2012) demonstrated that even when using ensemble-derived covariances, additional variance rescaling is often necessary to correct systematic underestimation [23]. The reliance on the empirical tuning of weight coefficients between ensemble and static covariances further limits adaptability, presenting operational challenges in high-dimensional systems.
## 5 Immersive Interface Testbed: An Evaluation Example
### Measurements and Questionnaires  
The usability evaluation was performed in two parts: performance-related measures and user subjective evaluation using a questionnaire.  
Regarding the analysis of the performance, we measured the following variables directly from the instrumented object and/or using a third observer to keep records.  
_Time_: task completion time for the procedure. This integrates delays due to depth perception errors.  
_Hits_: collisions with objects due to erroneous spatial perception.  
_Path following precision_: 3D path following precision with the tip of the stick (electric contact and time to accomplish)  
The goal is to identify task performance manipulation errors due to impaired visualization, shaking, etc.  
For the subjective evaluation, a questionnaire was created inspired by the IBM Computer Usability Satisfaction Questionnaire [103] and based on the presence questions of [PERSON], [PERSON], and [PERSON] [104, 105]. The participant feedback classified, on a seven point Likert scale, factors like usability, easiness, control precision, fatigue, realness, telepresence, and embodiment feeling. Questions were translated into the Portuguese language to simplify their understanding. The eight questions to answer were divided into two groups as follows:  
**Usability and task load questions:**  
**Q1**: I visualized the workspace... (1 = without any difficulties, 7 = with difficulties)
**Q2**: Was the task triing? (1 = Not triing, 7 = Very triing)
**Q3**: I managed to manipulate objects quite accurately (1 = Not at all, 7 = Very much)
**Q4**: The workspace visualization did not difficult object manipulation (1 = Disagree, 7 = Agree)
**Immersion presence questions:**  
**Q5**: I forgot that I used an indirect technological visualization device (1 = Disagree, 7 = Agree)
**Q6**: I had a clear perception and total control of stick's movements? (1 = Not at all, 7 = Yes totally)
**Q7**: I perform better when: (1 = I move my head, 7 = I do not move my head)
**Q8**: I know where the objects are because I can touch them. (1 = Disagree, 7 = Agree)
## 4 Analysis of Calculation Results
### Rock-Breaking Load of Arcing-Blade Cutters
At a penetration of 2 mm, there was no synergistic effect between the cutters for different cutter spacings, causing cutter 2 to independently function like a single unit in rock cutting. Consequently, the rock-breaking  
Figure 8: The average values of three-directional loads: (**a**) vertical force; (**b**) rolling force; and (**c**) lateral force. Figure 7: Three-directional loads: (**a**) vertical force; (**b**) rolling force; and (**c**) lateral force. load did not change with an increase in the cutter spacing. Conversely, when the penetration was 4 mm, the smaller cutter spacing enabled the two cutters to synergistically cut the rock. As the cutter spacing exceeded a certain value, this synergistic effect weakened or disappeared. Observably, at a cutter spacing of 60 mm, the synergistic effect vanished, causing cutter 2 to be similar to a single unit in rock cutting, thereby maintaining a stable rock-breaking load, as shown in Figure 8.  
When the penetration reached 6 mm, it can be found that the synergistic effect between cutter 1 and cutter 2 was eliminated when the cutter spacing increased to 70 mm, and the rock-breaking loads stayed stable with a further increase in the cutter spacing, as shown in Figure 8. This indicates that the cutter spacing had a significant impact on the rock-breaking load for a large penetration. Moreover, the difference in the rock-breaking load between cutter 2 and cutter 1 was very small when the synergistic effect disappeared, as depicted in Figure 7. Consequently, the cutter spacing associated with the synergistic effect could reduce the rock-breaking load of cutter 2 for a large penetration. Specifically, when the penetration level is fixed, a smaller cutter spacing strengthens the synergistic effect, while a larger cutter spacing weakens or even eliminates the synergistic effect for two cutters, leading to the stabilization of the rock-breaking loads with an increase in cutter spacing.
## 3 Simulation Experiments
### Model Evaluations
#### 3.5.2 Road Tests  
The comparison table of model evaluation indexes after testing using data from a total of 561 branch roads and 729 main roads in some areas of Xi'an is shown in Figure 16.  
As can be seen from Figure 16, after road correlation using the SVR model modeling, its overall accuracy has been close to the average accuracy of the overall model, and its accuracy has been able to meet the needs of the Xi'an city branch roads for short-term traffic flow prediction on the road.
# A Real Case of Implementation of the Future 5G City
## 3 Future 5G City Use Cases
### Access point densification  
In order to provide sufficient coverage and/or capacity, small cells have to be added to the network infrastructure, and some vehicles can complement the public network by becoming self-deployed moving cells (for example, in case of emergency or network unavailability). However, the coexistence of multiple cell-layers requires careful investigation on resource usage and generated interference, and hence requires coordination/cooperation among cells. An effective way to manage interference limiting complexity and signaling overhead is enhanced inter-cell interference coordination (eICIC) [27; 29]. An extension of the eICIC concept is under evaluation in enhanced studies. In particular, we considered a moving small cell, deployed where and when needed to provide dedicated access to vehicular users, which operate on the same channel and on the same area of the macrocell belonging to the conventional network. The two cells access the resources on a time-division basis, in some time slots in exclusive mode (interference free slots, \(N_{M}\) and \(N_{S}\)) and in others in jointly mode (slots affected by mutual interference \(N_{I}\)), see Figure 5. The total capacity of the system is maximized if the joint allocation of users in the subframes and subframes distribution are suitably performed. For example, in comparison with a fixed partitioning of the slots among macrocells and moving small cells, we have a reduction of almost 50% of the outage rate in an overloaded situation.  
Figure 4: Experimentation of I2V link using visible light communication (VLC).
## 8 Security Analysis
### Security Properties  
Through our approach, we aim to achieve the following two security properties:* **End-to-end data flow integrity:** This concept was first formalized in [9]. Our aim is to achieve not only the integrity of each exchanged message but also the integrity of the entire flow of exchanged messages.
* **Accountability:** This property ensures that publishers and brokers cannot deny having sent a flow of messages after it has been sent. Concerning the **accountability** property it implies the following:
* the flow of messages published by the publisher/broker can be traced back to it in a verifiable manner.
* the publisher/broker cannot deny the publication of a flow once it has been sent. Concerning the **end-to-end data flow integrity** property, it was originally defined in a weak sense and in a strong sense [9]. In our analysis, we consider the weak one. Specifically, it consists of the following properties:
* **Completeness:** all the messages sent by a publisher are also received by the subscribers;
* **Correctness:** all the messages received by the subscribers are correct, in the sense that, for instance, there are no new messages added by an attacker, i.e., messages that are not present in the original message flow sent by the publisher;
* **Weak Liveness:** the order of the messages received by the subscribers is preserved.
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 5 Experimental Results
### Packet Size  
We also measured the size of the transmitted packets (PUBLISH, PUBREC, PUBREL, and PUBCOMP) within the publishing process. As an MQTT packet comprises a fixed header, variable header, and payload, we conducted separate measurements for these three components. Given that our previous experiments involved the encapsulation of an MQTT packet within a CM event, the overall packet size encompassed the CM event header as well. The size of the CM event header remained constant for all types of packets including PUBLISH, PUBREC, PUBREL, and PUBCOMP. When comparing E-MQTT to MQTT, it was observed that E-MQTT led to an increase in the PUBLISH packet size due to the inclusion of a mandatory minimum number of response packets. Moreover, E-MQTT introduces additional fields such as an optional list of designated subscriber IDs. In the case of E-MQTT, the synchronous and asynchronous modes share the same packet format, resulting in uniform packet sizes. Figure 17 illustrates the outcome of our measurement results. Specifically, in the context of the PUBLISH packet, E-MQTT entailed an augmentation of 2 bytes in the variable header to accommodate the minimum number of waiting packets.  
Figure 16: Comparison of broker-subscriber delay results.  
However, for the other packet types, including PUBREC, PUBREL, and PUBCOMP, the sizes remained consistent between E-MQTT and MQTT.  
Table 3 provides insight into the packet sizes of the PUBLISH packet in scenarios where the E-MQTT publisher incorporates optional subscriber IDs. Notably, each subscriber ID was represented by a single UTF-16 digit. The table's second column displays the count of designated subscribers. As is inherent to E-MQTT, the size of its PUBLISH packet consistently exceeded that of MQTT by 2 bytes. This discrepancy arises from the inclusion of the number of pre-specified waiting packets (_waited_packet_num_) within E-MQTT's PUBLISH packet. As the PUBLISH packet of E-MQTT stores a list of subscribers, the packet size increases whenever the publisher adds a subscriber ID. As such, it is prudent for the E-MQTT publisher to exercise caution with regard to an excessive number of subscribers, as this could lead to a significant enlargement in packet size.