esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 10 Implementation Challenges and Emerging Solutions
Unlike electronics, where multi-project wafer (MPW) services are standardized and frequent, silicon photonics faces long fabrication delays, infrequent access, and limited support for novel designs. Most MPW programs prioritize high-volume industrial clients, restricting research prototyping and increasing both cost and risk. Initiatives like AIM Photonics (USA), JePPIX (EU), and EUORPACTICE aim to broaden access via standardized PDKs and subsidized MPW runs. However, PDK limitations and pipeline rigidity persist. Greater foundry support and open-access, modular design flows are needed to make the photonic ecosystem as agile and scalable as its electronic counterpart, especially for Cloud, Fog, and IoT applications [49]. Finally, deployment-specific constraints--especially in Fog and Edge environments--impose tight requirements on size, energy, and environmental resilience. While Cloud data centers can accommodate large-scale cooling systems and redundant designs, Fog nodes must be compact, power-efficient, and ruggedized for varied operating conditions [10]. This necessitates modular packaging solutions, thermally adaptive components, and PIC architectures that can gracefully degrade or self-reconfigure under stress. These features are critical to enable robust operation in mobile, outdoor, or constrained environments. In summary, while PICs hold immense promise for high-speed, low-energy, and scalable communication and computation, their widespread deployment depends on overcoming multi-layered challenges. The integration of design automation, manufacturing innovation, thermal control strategies, and self-tuning mechanisms will be crucial in transitioning PICs from research labs to large-scale deployment in Cloud and Fog infrastructures.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 1 Introduction
### Paper Structure and Organization  
Traditional cryptography solutions, while crucial, cannot address all security challenges in 5G networks, not only because of their inherent complexity and computational demands but primarily because many attacks happen in the initial phase of the connection, when communication is usually unprotected. This paper explores the role of PHY layer security in 5G and its potential to safeguard communications against evolving threats. Therefore, we focus primarily on the vulnerabilities of the lower layers, such as the PHY layer, and on the potential use of the PHY for authenticating legitimate communication devices. This article is divided as follows: In the Section 2, we review the technology background of the 5G PHY layer. In Section 3, we focus on possible basic threats to mobile networks such a jamming, smart jamming, and spoofing/overshadowing attacks, and we complement it with the basic countermeasure approaches. Section 4 is focused on Multiple-Input Multiple-Output (MIMO)-specific attacks such a Beam Alignment (BA) jamming, pilot contamination, and user tracking, together with the most common mitigation techniques. Hardware and open-source software tools, which can be used to build 4G/5G testbeds for experimentation, are introduced and thoroughly compared in Section 5. Sections 6, 7, and 8, respectively, present surveys of machine-learning techniques and methods that rely on localization information and device behavior to detect the most common threat--the rogue base station. Section 9 then discusses the PHY layer security related to the future technology candidates for beyond 5G networks. Finally, Section 10 concludes the paper.
## 5 Securing ITS Applications: A Practical Case Study
### Lessons Learned  
During our implementation of the latest version of TS 103 097 [24], we have noticed that the previously identified flaws [95], such as the unbounded limit of the integer variable length structure or the multiple payload support, were already fixed. During the last fourth ETSI Plugtest event, we had not experienced any major flaws with our current implementation, but we will underline below some possible issues or misunderstandings in the current draft document [24]. First, the geographical validation of certificates is not yet very clear; especially the one related to the identified region where no information is  
Figure 14: Cryptographic performances of an ETSI ITS security software implementation (ECDSA) running on an Intel CPU at 3 GHz.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Cryptographic & \multicolumn{2}{c}{Intel CPU at 1 GHz} & \multicolumn{2}{c}{Intel CPU at 3 GHz} \\ Algorithms & SN-ENCAP & SN-DECAP & SN-ENCAP & SN-DECAP \\ \hline ecdsa\_nistp192\_with\_sha256 & 3.56 \(\pm\) 0.10 & 24.43 \(\pm\) 0.23 & 0.12 \(\pm\) 0.006 & 0.67 \(\pm\) 0.014 \\ ecdsa\_nistp224\_with\_sha224 & 5.84 \(\pm\) 0.28 & 34.91 \(\pm\) 0.46 & 0.35 \(\pm\) 0.022 & 1.02 \(\pm\) 0.048 \\ ecdsa\_nistp224\_with\_sha256 & 4.73 \(\pm\) 0.17 & 33.82 \(\pm\) 0.33 & 0.12 \(\pm\) 0.003 & 0.73 \(\pm\) 0.013 \\ ecdsa\_nistp256\_with\_sha256 & 6.17 \(\pm\) 0.16 & 44.03 \(\pm\) 0.31 & 0.26 \(\pm\) 0.012 & 1.22 \(\pm\) 0.047 \\ ecdsa\_nistp384\_with\_sha384 & 14.55 \(\pm\) 0.20 & 143.63 \(\pm\) 0.5 & 0.52 \(\pm\) 0.019 & 2.98 \(\pm\) 0.036 \\ \hline \hline \end{tabular}  
* NIST-recommended ECDSA schema. \({}^{\text{b}}\) ETSI ITS-compliant ECDSA schema. \({}^{\text{c}}\) IEEE 1609.2 compliant ECDSA schema.  
\end{table}
Table 5: ETSI TS 103 097 average cryptographic operation delay (milliseconds; 95% confidence interval).  
yet available for such structures and for how the received locations will be checked. Second, the signature verification step cannot be achieved until the received packet is parsed. This is due to the fact that the signature structure is dynamic and extendable, so the user should know the trailer field structure before being able to compute the message digest. This may lead to malicious code injection-based attacks, as many of the data structures are of variable lengths.The last point we have encountered is that the ETSI TC ITS security architecture seems to be vulnerable to denial of service (DoS) attacks. As described in the ETSI draft document, the receiver should always send its own certificate (or certificate chain) if it encounters its own certificate hashId8 inside a request_unrecognized_certificate message. Malicious users can thus keep sending forged packets to force the receiver to send its certificate continuously, which could exhaust the local resources and the allocated network bandwidth.
# A Comprehensive Survey on Knowledge-Defined Networking
## 5 Control Plane
### Interfaces and Sub-Planes Connected with the Controller
#### 5.2.5 Network Hypervisors  
A network hypervisor's primary function is to provide virtualization, which enables the installation of different network operating systems on a single physical server. By allowing several virtual computers to share the same hardware resources, hypervisors perform this. As a result, hypervisors effectively cut the cost of the infrastructure. Virtual machines' ability to be moved, created, or destroyed on demand also makes it possible to offer elastic services. The network infrastructure must be able to accommodate any network topologies and addressing methods in order to allow complete virtualization. However, it is practically challenging to realize such infrastructure. Topology and address virtualization are made possible by network virtualization, which also enables resource provisioning, management, and monitoring over virtual networks.  
With the use of an abstraction layer to slice data based on ready-made OpenFlow-capable switches, FlowVisor enables many logical networks to share the same OpenFlow networking infrastructure, enabling the coexistence of numerous and heterogeneous networks. Traffic, CPU, forwarding tables, bandwidth, and topology are the dimensions taken into account for slicing in FlowVisor. Each network slice supports a controller, allowing several controllers to coexist in the same physical network while each controller is free to operate on its own network slice [301].  
Another network hypervisor called OpenVirtex creates virtual networks using control functions, topology, and address virtualization, which is similar to FlowVisor, which acts as a proxy between the network operating system and forwarding devices [302].  
While FlowVisor is a full virtualization technology, FlowN provides container-based virtualization that is designed to be scalable and provides the opportunity to share a controller to manage multiple domains [303].  
HyperFlex decomposes the hypervisor into functions for virtualizing KDN networks and also has a control plane isolation function for control plane virtualization such that control plane resources are correctly shared between each virtual KDN network while, at the same time, protecting the resources from exhaustion [304].  
TeaVisor is a network hypervisor that guarantees bandwidth isolation by tackling the overloaded link problem, which has three components: path virtualization, bandwidth reservation, and path establishment [305].  
VMware is another network virtualization platform where network functions are embedded in the hypervisor and distributed across the environment, which provides the entire network model in software. This network virtualization platform allows the creation of virtual networks, each with its own service model and topologies, addressing architectures having the same physical network [306].
## 4 Experimental Section and Results Analysis
### Process Parameter Design Based on Bayesian Optimization  
In this experiment, the model was applied to four germanium single-crystal datasets. Due to the inherent time delay associated with adjusting the average liquid level temperature, the optimized process parameter was the actual crystal growth rate. The hyperparameters for Bayesian optimization were set with a maximum of 20 iterations and the acquisition function used Equation (9). The target prediction value was set to 104 and the objective of Bayesian optimization was to make the predicted diameter value of model as close as possible to the target prediction value. Figure 8 shows the prediction results.  
On the four datasets K1, K2, K3 and K4, the RMSE values are 0.21, 0.09, 0.13 and 0.05, with an average RMSE of 0.12. From Figure 8, it can be observed that the recommended  
Figure 8: Comparison between predictive results of recommended pulling rate and the real one on four different datasets: K1, K2, K3 and K4.  
Figure 7: Predictive results of OASVR-Elk on four different datasets: K1, K2, K3 and K4.  
points obtained through Bayesian optimization mostly remain within a range of \(\pm 0.5\) from a diameter value of 104. Additionally, it is noticeable from the figure that the first half of the iterations for the recommended points show significant variations, while, in the later stages, they tend to approach the actual values. This suggests that the adaptive learning rate helps to balance exploration and exploitation effectively. Comparing the trends in actual values and predicted values, such as the section between 100 and 200 in the K1 chart and the section between 50 and 100 in the K3 chart, the actual diameter values start above 104. With the changes in recommended points, the actual values gradually decrease and approach 104, ultimately maintaining the actual diameter values within the allowable error range of the target diameter value.
# Efficient Runtime Firmware Update Mechanism for LoRaWAN Class A Devices
## 3 Developed System
### Application Server  
Application servers play a crucial role in implementing an IoT system, being responsible for processing, storing and presenting data sent by end devices. The application server developed in this work is of the machine-to-machine (M2M) type, meaning there is no user web interface on the application server.  
At the core of the application server is the middleware (Figure 7), which serves as a vital interface between the network server and the database. Upon receiving a JSON frame via HTTP POST method from the network server, the middleware extracts data from the frame fields. Based on the type of data received from the LoRaWAN network server, it performs specific and differentiated actions. The middleware is responsible for routing data from end devices; for example, if the received data pertain to device registration, Port 4, the middleware directs the data to the device registration table in the database. Conversely, if the data are related to an event, they are directed to the event registration table. Additionally, the middleware checks whether there are any data in the database to be forwarded to the end device that initiated the message reception by the network server. In the downlink, the middleware segments messages destined to end devices, ensuring they are formatted according to the maximum payload size supported by the LoRaWAN network (Table 1).
# Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya
## 3 Results
### Classification of Baseline Soil Erosion by Severity  
Similar to the study by reference [81], the mean annual soil erosion rate was grouped into six levels of severity: slight, moderate, high, very high, severe and very severe (Table 4). Most of the investigated area had a slight severity erosion rate, while approximately 3.7% of the total area fell within the severe and very severe categories.
## 7 Regularization
### \(\ell_{2}\) Regularization (Weight Decay)  
The optimization goal with \(\ell_{2}\) regularization is to minimize the sum of losses, \(\sum_{i}l_{i}(W)\), subject to the following constraint on the 2-norm (i.e., the Euclidean norm): \(\|W\|_{2}\leqslant r\). Geometrically, this means that we want weights to be kept within a ball (the dimensionality of the ball being equal to the number of weights) of radius \(r\). Commonly, one incorporates the constraint into the cost function and applies the following equivalent form [55; 56]:  
\[\operatorname{err}(W)=\sum_{i}l_{i}(W)+\frac{1}{2}\lambda\,\|W\|_{2}^{2}, \tag{107}\]  
where \(\lambda\geqslant 0\) is a penalty hyperparameter controlling the strength of regularization. There exists a 1:1 correspondence between \(r\) and \(\lambda\). Large \(\lambda\)s translate into small radiuses.  
Taking a derivative of (107) with respect to any \(w_{k}\) yields  
\[\frac{\partial\operatorname{err}(W)}{\partial w_{k}}=\sum_{i}\frac{\partial l _{i}(W)}{\partial w_{k}}+\lambda w_{k}. \tag{108}\]  
Therefore, in the simple SGD scenario, the update of a single weight can be carried out in the following manner:  
\[w_{k}:=w_{k}-\eta\sum_{i}\frac{\partial l_{i}(W)}{\partial w_{k}}-\eta \lambda w_{k}. \tag{109}\]  
Such an update rule is very simple to implement. Other advanced SGD variants (Adam, Adamax, AMSGrad, etc.) pose no difficulties either because \(\
abla\), presented in various formulas in Section 6, should now be understood as the gradient of the whole cost function, including the regularization term, i.e., \(\
abla=\left(\sum_{i}\partial l_{i}(W)/\partial w_{k}+\lambda w_{k}\right)_{k}\), generated for all \(k\). It is also easy to see how (109) tries to _shrink_ weights at each update step [55], i.e., pull them towards zero, owing to the last summand, \(-\eta\lambda w_{k}\).  
Finally, it is worth pointing out a potential nomenclature ambiguity. In the literature on neural networks, \(\ell_{2}\) regularization is often known by the name of _weight decay_[57; 58; 59]. Please note that this should not be confused with _learning rate decay_--a scheduling technique [60; 61], present, e.g., in Keras and PyTorch, that is, in fact, _not_ a regularizer.
## 4 Design Implementation
### FPGA Bus Components
#### 4.3.1 Register-Based Content-Addressable Memory  
Based on our understanding and the previous research [4], it is known that the traditional Non-Repeating PRNG (NR-PRNG) implemented solely in pure software cannot meet the efficiency requirements for real-time stream processing as proposed in this paper. Upon investigation, one feasible approach is to utilize Content-Addressable Memory (CAM) for data comparison by hardware acceleration [30]. Unlike traditional memory where the processor inputs an index address and the memory outputs the data byte stored at that location, CAM operates differently. In CAM, the processor inputs a data byte and determines whether this input data are already stored in the memory, outputting the result of the match operation. CAM implemented in FPGA can be categorized into two types: Register-based CAM (REG-CAM) and RAM-based CAM (MEM-CAM) [55]. We have integrated this REG-CAM module as part of the system bus (as indicated in the green box in Figure 8), providing the REG-CAM with a bus interface for the micro-processor to access its relevant control and status registers. This allows the micro-processor to input key stream blocks generated by the KS-Generator controller (as shown in the other green box in Figure 8) into the REG-CAM controller. Thees input data are then checked to determine if the key stream block has been used before. If it has, another key stream block is generated and tested until a non-repeating and usable key stream block is obtained. This key stream block is then stored to be used for encryption with the plaintext data stream, as elaborated in the subsequent Section 5 \"Experimental Results\".
# Combinatorial Generation Algorithms for Some Lattice Paths Using the Method Based on AND/OR Trees +
## 3 Results
### Combinatorial Generation Algorithms for Dyck Paths
#### 3.2.2 AND/OR Tree Structure  
Since Equation (4) satisfies the requirements of the applied method, the corresponding AND/OR tree structure for \(C_{n}\) can therefore be constructed (see Figure 5).  
For this AND/OR tree structure, there is the following initial condition:  
* Each node labeled \(C_{0}\) is a leaf node in the AND/OR tree for \(C_{n}\).  
Figure 6 presents an example of the AND/OR tree structure for \(C_{n}\) where \(n=3\). The total number of its variants is equal to \(C_{3}=5\).  
For a compact representation, a variant of the AND/OR tree for \(C_{n}\) is encoded by a sequence \(v=(I,v_{1},v_{2})\), where the following apply:  
* An empty sequence \(v=()\) corresponds to the selection of a leaf node labeled \(C_{0}\);
* \(I\) corresponds to the selected value of \(i\) in the AND/OR tree for \(C_{n}\);
* \(v_{1}\) corresponds to the variant of the subtree of the node labeled \(C_{I}\) (the left subtree);
* \(v_{2}\) corresponds to the variant of the subtree of the node labeled \(C_{n-1-I}\) (the right subtree).  
Figure 5: An AND/OR tree for \(C_{n}\).  
Figure 6: An AND/OR tree for \(C_{3}\).  
**Theorem 2**.: _There is a bijection between the set of Dyck \(n\)-paths and the set of variants of the AND/OR tree for \(C_{n}\)._  
Proof.: The total number of Dyck \(n\)-paths is equal to \(C_{n}\). The total number of variants of the AND/OR tree for \(C_{n}\) presented in Figure 5 is also equal to \(C_{n}\). Therefore, it is possible to associate each such lattice path with one specific variant of the AND/OR tree for \(C_{n}\). A bijection between the set of Dyck \(n\)-paths and the set of variants of the AND/OR tree for \(C_{n}\) is defined by the following rules:  
* Each selected child of the OR node labeled \(C_{n}\) determines the addition of one East-step and one North-step to the Dyck \((n-1)\)-path that merges the Dyck \(I\)-path obtained by the subtree of the node labeled \(C_{I}\) and consisting of \(2I\) steps and the Dyck \((n-1-I)\)-path obtained by the subtree of the node labeled \(C_{n-1-I}\) and consisting of \((2n-2I-2)\) steps: the resulting Dyck \(n\)-path is \((s_{1},\ldots,s_{2I},E,s_{2I+2},\ldots,s_{2n-1},N)\);
* The subtree of the node labeled \(C_{I}\) (the left subtree) determines the Dyck \(I\)-path of the form \((s_{1},\ldots,s_{2I})\);
* The subtree of the node labeled \(C_{n-1-I}\) (the right subtree) determines the Dyck \((n-1-I)\)-path of the form \((s_{2I+2},\ldots,s_{2n-1})\);
* Each leaf node labeled \(C_{0}\) determines the empty lattice path \(()\).  
The algorithms that implement the developed bijection rules have polynomial time complexity \(O(n^{2})\), since they make \(2n\) recursive calls, where each such recursive call requires one pass to fill a sequence of \(2n\) elements. An example of applying these bijection rules is presented in Table 2.
# A Multi-Scale Approach for Modelling Airborne Transport of Mucosalivary Fluid
## 2 Governing Equations
### Eulerian Phase  
Compressible Reynolds Averaged Navier-Stokes (RANS) equations are solved for Eulerian phase:  
\[\begin{split}&\frac{\partial\overline{\rho}}{\partial t}+\frac{ \partial}{\partial x_{j}}\big{(}\overline{\rho}\dot{u}_{j}\big{)}=s_{m},\\ &\frac{\partial}{\partial t}\big{(}\overline{\rho}\dot{u}_{i} \big{)}+\frac{\partial}{\partial x_{j}}\big{(}\overline{\rho}\dot{u}_{i}\dot{ u}_{j}\big{)}=-\frac{\partial\overline{\rho}}{\partial x_{i}}+\frac{\partial \dot{\tau}_{ij}}{\partial x_{j}}+\overline{\rho}\dot{g}\delta_{i\beta}+s_{m,i },\\ &\frac{\partial}{\partial t}\big{(}\overline{\rho}\dot{E}\big{)} +\frac{\partial}{\partial x_{j}}\big{(}\overline{\rho}\dot{u}_{j}\big{)}=- \frac{\partial q_{j}}{\partial x_{j}}+\frac{\partial}{\partial x_{j}}\big{(} \tilde{u}_{i}\tilde{\tau}_{ij}\big{)}+s_{e},\\ &\frac{\partial}{\partial t}\big{(}\overline{\rho}\tilde{Y}_{k} \big{)}+\frac{\partial}{\partial x_{j}}\big{(}\overline{\rho}\tilde{u}_{j} \tilde{Y}_{k}\big{)}=-\frac{\partial m_{k,j}}{\partial x_{j}}+s_{Y_{k}},\end{split} \tag{1}\]  
where \(\overline{\rho}\), \(\tilde{u}_{i}\), \(\overline{\rho}\) and \(\tilde{Y}_{k}\) denote density, velocity component in \(x_{i}\) direction, pressure, temperature and chemical specie \(k\) mass fraction. \(\dot{E}\) and \(\bar{H}\) are, respectively, the total internal energy and enthalpy. As usual in CFD community, the overbar and the tilde are filtering operators which are introduced for unweighted and density-weighted averages.  
In Equation (1) we can find several unclosed terms which handled in a standard way, i.e., rheological equation for newtonian fluids, Fourier law and Fick law are employed. Conversely, Reynolds stresses are modelled using SST \(k\)-\(\omega\) model developed by [PERSON] (19). It is also important to note that the source terms in Equation (1) are the mathematical expression of the Lagrangian and Eulerian phases coupling (18).