esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Uploading Pattern of Source Nodes
#### 2.2.1 Passive Uploading Pattern  
In the passive uploading pattern, the mobile sink needs to traverse in the network to collect data by short range communication. To minimize energy consumption, the mobile sink needs to visit each sensor node. That means each data packet can be transmitted to the sink directly. The energy used for packets relaying is saved in this way. However, the limited velocity of the mobile sink may lead to unacceptable time delay. Moreover, limited memory space may also lead to overflow. To overcome these problems, the idea of cluster is proposed. Sensory data is transmitted to the cluster head node in each cluster. The mobile sink just needs to visit these cluster heads. This approach is associated with a trade-off between energy consumption, time latency, and memory space usage rate.  
In [3], [PERSON] et al. propose the bounded relay hop mobile data gathering (BRH-MDG) algorithm. It exploits a balance between the relay hop count of each cluster and the moving tour length of the mobile sink. This balance gets a trade-off between energy consumption and data gathering latency. In this algorithm, a subset of sensor nodes are selected as polling points (PPs). They buffer local sensory data and upload the data to the mobile sink when it arrives. The main point of this paper is that any data packet relay to corresponding PPs is bounded within a given number of hops.  
BRH-MDG can reduce latency, but it does not consider the deadline of sensory data. Hence, a deadline is introduced in [16]. Weighted rendezvous planning (WRP) is proposed by [PERSON] et al. in [16]. This algorithm is mainly used in delay-sensitive applications. This means that all sensory data has a deadline. Mobile sink only visits rendezvous points (RPs), and other nodes upload sensory data to RPs. Different from BRH-MDG, WRP does not need to have an uploading bound and a shortest traverse path. WRP just needs to visit more RPs in a constraint time. Then energy can be saved. In an initial situation, the first RP is the location of the sink. There are some rounds in WRP. In each round, each node calculates a weight value. The weight value is the multiplication of packets number relayed and hop distance to the closest RP. The sensor node with the highest weight value is chosen as an RP. This RP is added in a traveling path. Sink judges whether the moving time of travel path is beyond the constraint time. If the moving time is still in the constraint time, the next round will be started. The calculation of weight value can improve the performance of data collection. However, the residual energy of node is not added in calculation. Hence some nodes with a higher weight value may have a lower residual energy. It affects the lifetime and the balance of network.  
Although these algorithms can reduce latency to some degree, the latency of uploading is still hard to accept in an application where fresh data is very important. The complexity of planning tour is always high. Hence the pattern of passive uploading is not suitable for delay-sensitive application scenarios.
## 3 Results and Discussion
### Rubber Composites Preparation and Characterization
#### 3.2.2 Dynamic-Mechanical Shear Properties  
Dynamic-mechanical properties were determined for the composites with silica and the hybrid silica/HSAG filler system, by applying the sinusoidal stress in the shear mode, performing strain sweep experiments as described in the Materials and Method Section 2.8 using a Rubber Process Analyzer (RPA). Storage modulus (G') at minimum and maximum strain, maximum loss modulus (G''), and maximum Tan Delta (\(\mathrm{G''max}/\mathrm{G''max}\)) were obtained for the rubber composites incorporating Silica, Silica/HSAG, and Silica/HSAG-SP. Results are reported in Table 4 and Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Composite** & **Silica** & **Silica/HSAG** & **Silica/HSAG-SP** \\ \hline \(\mathrm{G^{{}^{\prime}}}_{0.1\%}\) & 1.61 & 1.42 & 1.40 \\ \(\mathrm{\Delta G^{{}^{\prime}}}=(\mathrm{G^{{}^{\prime}}}_{0.1\%}-\mathrm{G^{{}^{ \prime}}}_{2\%})\) & 0.86 & 0.72 & 0.72 \\ \(\mathrm{G^{{}^{\prime\prime}}}_{\mathrm{max}}\) & 0.14 & 0.12 & 0.12 \\ \(\mathrm{tan~{}delta_{max}}\) & 0.15 & 0.14 & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Storage modulus G’ at minimum (0.1%) shear strain, \(\mathrm{\Delta G^{{}^{\prime}}}(\mathrm{G^{{}^{\prime}}}_{0.1\%}-\mathrm{G^{{}^{ \prime}}}_{25\%})\), maximum loss modulus \(\mathrm{G^{{}^{\prime\prime}}}_{\mathrm{max}}\), and maximum \(\mathrm{tan~{}delta_{max}}\) (\(\mathrm{G^{{}^{\prime\prime}}}_{\mathrm{max}}/\mathrm{G^{{}^{\prime}}}_{ \mathrm{max}}\)) from strain sweep tests of the rubber compounds of Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Composite** & **Silica** & **Silica/HSAG** & **Silica/HSAG-SP** \\ \hline \(\mathrm{M_{L}}\) (\(\mathrm{d}\mathrm{N}\mathrm{m}\mathrm{m}\)) & 3.2 & 2.8 & 2.6 \\ \(\mathrm{M_{H}}\) (\(\mathrm{d}\mathrm{N}\mathrm{m}\)) & 15.3 & 13.8 & 13.6 \\ \(\mathrm{M_{H}}-\mathrm{M_{L}}\) (\(\mathrm{d}\mathrm{N}\mathrm{m}\)) & 12.1 & 11.1 & 10.9 \\ \(\mathrm{t_{90}}\) (\(\mathrm{min}\)) & 10.9 & 10.6 & 10.7 \\ \(\mathrm{t_{S1}}\) (\(\mathrm{d}\mathrm{N}\mathrm{m}\)) & 3.2 & 3.2 &The use of HSAG in place of silica led to an appreciable reduction in G'\({}_{\gamma\text{min}}\), \(\Delta\)G', and G\"\({}_{\text{max}}\), to the same extent. A slightly lower value of tan delta was obtained for the composite with HSAG-SP. These findings can be reasonably explained with the reduction in the filler networking phenomenon.
## 4 Experimental Evaluation  
The performance of the machine learning models was evaluated using both the train-test split and 10-fold cross-validation approaches, as described in Section 3. The results highlight the effectiveness of the proposed methods in addressing the challenges of im  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Hyperparameter** & **Split** & **10-Fold** \\ \hline RF & n\_estimators & 112 & 57 \\  & max\_depth & 25 & 8 \\  & min\_samples\_split & 2 & 6 \\ \hline AdaBoost & n\_estimators & 169 & 91 \\  & learning\_rate & 0.22031826639728547 & 0.868649991657305 \\ \hline XGBoost & n\_estimators & 346 & 338 \\  & max\_depth & 4 & 6 \\  & learning\_rate & 0.08257351184613305 & 0.12486607179583395 \\  & subsample & 0.820421224719867 & 0.8398820455022838 \\  & colsample\_bytes & 0.6442582375605579 & 0.8885000829639664 \\ \hline LR & C & 0.43873275517235 & 3.2376253329526534 \\  & penalty & 11 & 11 \\  & solver & saga & saga \\ \hline ET & n\_estimators & 94 & 50 \\  & max\_depth & 16 & 34 \\ \hline DNN & layers & 4 & 4 \\  & units & 128 & 96 \\  & dropout\_rate & 0.2774088047541644 & 0.24552040101739658 \\  & learning\_rate & 0.0009501230980605523 & 0.00010682901273334459 \\  & batch\_size & 112 & 128 \\  & epochs & 45 & 34 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimized hyperparameters for split and 10-fold training.  
balanced multi-class intrusion detection. Key performance metrics, including precision, recall, accuracy, and F1-score, were used to evaluate model performance. Table 4 shows the execution time, in seconds, for the hyperparameter optimization and training of each model in both training approaches. The results highlight a critical trade-off between computational efficiency and model performance. While models such as Random Forest, Extra Trees, and XGBoost demonstrated perfect classification performance with relatively low computational costs, DNN required a significantly higher processing time, particularly during the hyperparameter optimization and training. In contrast, Random Forest and Extra Trees maintained both high performance and efficiency. In addition, AdaBoost and Logistic Regression also revealed some challenges in computational efficiency.
## 5 Securing ITS Applications: A Practical Case Study
### ETSI Security Layer: TS 103 097
#### 5.3.1 Security Headers  
ETSI TS 103 097 [24] defines different security headers and formats to ensure the interoperability of the different elements and security information that are being exchanged between the ITS stations for security purposes. The main security header is the SecuredMessagestructure, which specifies how to encode a generic security message, which is itself encapsulated inside a GeoNetworking packet, as shown in Figure 12.  
As shown in Listing 1, a SecuredMessage starts with a one-octet protocol_version field that should be incremented each time the security standard is updated. The current protocol version is two, as defined by the latest ETSI TS 103 097 [24] specifications. Then, a variable-length vector header_fields is defined, which shall contain the different information required by the security layer [24], such as generation_time (0), expiration (2), generation_location (3), its_aid (5), signer_info (128), encryption_parameters (129),  
Figure 12: Secured GeoNetworking packet structure as defined in ETSI TC ITS. CAM, cooperative awareness message; DENM, decentralized environmental notification message.  
recipient_info (130), _etc._ Depending on the considered security profile, the sequence of these header fields should be encoded in ascending numerical order of their type values. Next, a message payload is included, which consist of a payload type (_i.e._, unsecured, signed, encrypted, signed_external or signed_and_encrypted) followed by a variable-length vector containing the actual message payload (e.g., CAM, DENM, _etc._).  
```
Struct{ uint8 protocol_version; HeaderFieldheader_fields<var>; Payloadpayload_field; TrailerFieldtrailer_fields<var>;}SecuredMessage;
```  
Listing 1: ETSI ITS SecuredMessage structure.  
According to the latest standard specifications, only one payload can be included in a SecuredMessage. Finally, a variable-length vector trailer_fields is encoded just after the payload. These trailer fields contain security information that is necessary to verify the messages integrity and authenticity using a signature. Depending on the considered security profile, the sequence of these trailer fields should be encoded in ascending numerical order of their type values. It should be noted that the exact content of a SecuredMessage is determined by the corresponding security profile (_cf._ Subsection 5.3.3), which will check its validity, prior to applying any security operations.
## 3 MHSA-1D CNN-Bi-LSTM Interference Cancellation Scheme
### Bi-Lstm  
The Bi-LSTM is an extension of the LSTM architecture designed to handle data with temporal dependencies by utilizing two independent input sequences that are fed into the same LSTM layer. The forward and reverse LSTMs of the Bi-LSTM are trained using FTN signal sequence data, resulting in a structure consisting of an input layer, a forward hidden layer, a reverse hidden layer, and an output layer as depicted in Figure 3.  
The specific implementation of the Bi-LSTM algorithm is detailed in Table 1[21]. The Bi-LSTM algorithm processes time-series data through an input sequence \(X=[x_{1},x_{2},\ldots,x_{T}]\), where each feature vector \(x_{t}\) is mapped to both forward and backward LSTM cells at time step \(t\). The core of the LSTM is its gating mechanism, which includes input gates \(i\), forget gates \(f\), output gates \(o\), and candidate cell states \(g\). These gates utilize weight matrices \(W\) and \(U\) along with bias \(b\) to regulate the flow and storage of information. The forward and backward hidden states \(h_{forward}[t]\) and \(h_{backward}[t]\) together generate the output \(h[t]\) of the bidirectional LSTM, effectively capturing both forward and backward dependencies in the sequence data while handling nonlinear relationships through activation functions such as Sigmoid and hyperbolic tangent (tanh).  
Figure 3: The structure of Bi-LSTM.  
Figure 2: The structure of 1D CNN (Different colors represent different filters).
# In Situ Interaction of Enterocin A/P with _Staphylococcus aureus_ SA5 in Goat Milk Lump Cheese
## 2 Materials and Methods  
### Goat Milk Lump Cheese Manufacturing Process and Sampling  
Goat milk lump cheese was manufactured in 10-L (L) vats using the standard technology for this type of cheese [4]. Pre-heated milk vats were divided into the control vat (CV) experimentally contaminated with the _S. aureus_ SA5 strain (isolated from mastitis milk in our laboratory, concentration of inoculum was 10\({}^{7}\) cfu/mL = 7.0 cfu/mL (log 10), the experimental vat (EV) inoculated with the SA5 strain and treated with Enterocin A/P (its inhibitory activity 12,800 arbitrary unit per mL, AU/mL against the SA5 strain) [13], and the reference vat (RV) (without the SA5 strain and Enterocin A/P). Appropriate amount of cream culture, rennet as well and 40% CaCl\({}_{2}\), was added in the amount calculated for 10 L. Curds were cut (15 min), scaled, pressed, and cheese lumps were formed. They were placed to drop off (18-20 \({}^{\circ}\)C) and cheeses were stored in a cold room for 7 days. Two independent replicates were performed (each cheese was checked in duplicate) and the experiment was reproduced once. Bacterial counts were expressed in colony forming units per gram. Sampling for microbial analysis was performed during cheese manufacturing, at the start of experiment at time S/0 h (meaning after inoculation and before the addition of Ent A/P), at 0h when Ent A/P was added, at 4 h, 24 h, then after storage at days 4 (96 h) and 7 (168 h). To check lactic acid, acidity (\({}^{\circ}\)SH/100/mL) and pH values, sampling was provided at 24 h, 72 h, and 96 h (cheese manufacturing).
## 4 Q3: What Are the Methodologies Used to Overcome the Challenges in UIoT?
### Methods to Overcome the Security Challenges in UIoT
#### 4.2.2 Methods to Prevent Jamming Attacks  
The existing techniques to prevent jamming attacks in UIoT are discussed herewith. In [109], [PERSON] et al. present a shortcoming identification calculation where nodes deliberately trade revelation and affirmative packets. In [110], [PERSON] et al. present a productive channel task conspire, an original cross-layer plan for helpful correspondence for jamming detection. Finally, in [111], [PERSON] et al. proposed utilizing the game-hypothetical investigation of sticking to UIoT and proposed a machine learning-based energy management mechanism to adapt to jamming attacks in UIoT networks. The associations between a UIoT and a responsive jamming device are defined as two jamming games.  
Experimentally Weighted Moving Average (EWMA) was proposed by [PERSON] et al. [112] as a measurable productive procedure for identifying little changes in time series information. It works by first characterizing an edge that portrays standard conduct before intermittently refreshing the normal of the noticed traffic. The EWMA algorithm can be the countermeasure for jamming attacks. The below expression shows how the EWMA is calculated:  
\[x(d)=\lambda.y(d)\ +\ (1-\lambda).\ x(d-1)\ d=1,2,3,\ldots N\]  
\(x(d)\) is the data with moving average time \(d\), \(\lambda\) is the parameter value between 0 and 1, \(y(d)\) denotes the signal \(y\) at a time '\(d\)', \(N\) is the number of observations in EWMA.
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 3 Wireless Network Architectures for Energy-Efficient CFL
### Non-Terrestrial-Based Architectures  
Aerial access networks (AANs) have emerged as a pivotal technology in 5G and beyond networks [69]. These networks play a versatile role across a range of wireless applications, including IoT data collection, wireless backhaul support, emergency response operations, and wireless-powered communication systems. Owing to their distinctive attributes, such as high flexibility, mobility, and adjustable altitude, AANs can function as dynamic BSs, enhancing the capacity and coverage of wireless networks while optimizing energy efficiency [70].  
Driven by these advantages, integrating aerial networks with FL presents significant potential to enhance system performance [71]. By deploying aerial platforms, FL training can be extended across devices in remote or dynamic locations, supporting applications like smart agriculture, disaster response, and environmental monitoring [71; 72]. ANNs not only provide ubiquitous coverage but also contribute to energy efficiency, a critical factor in FL systems. However, since aerial platforms often operate with limited energy resources, it is essential to optimize energy use in communication, flight, and processing to maintain sustainable FL operations.  
This subsection delves into three key aerial platforms for FL: UAVs, high-altitude platform systems (HAPSs), and low Earth orbit (LEO) satellites highlighting their roles, associated challenges, and optimization strategies for promoting energy efficiency.
# Provisioning of Fog Computing over Named-Data Networking in Dynamic Wireless Mesh Systems
###### Abstract  
Fog computing is today considered a promising candidate to improve the user experience in dynamic on-demand computing services. However, its ubiquitous application would require support for this service in wireless multi-hop mesh systems, where the use of conventional IP-based solutions is challenging. As a complementary solution, in this paper, we consider a Named-Data Networking (NDN) approach to enable fog computing services in autonomous dynamic mesh formations. In particular, we jointly implement two critical mechanisms required to extend the NDN-based fog computing architecture to wireless mesh systems. These are (i) dynamic face management systems and (ii) a learning-based route discovery strategy. The former makes it possible to solve NDN issues related to an inability to operate over a broadcast medium. Also, it improves the data-link layer reliability by enabling unicast communications between mesh nodes. The learning-based forwarding strategy, on the other hand, efficiently reduces the amount of overhead needed to find routes in the dynamically changing mesh networks. Our numerical results show that, for static wireless meshes, our proposal makes it possible to fully benefit from the computing resources sporadically available up to several hops away from the consumer. Additionally, we investigate the impacts of various traffic types and NDN caching capabilities, revealing that the latter result in much better system performance while the popularity of the compute service contributes to additional performance gains.  
fog computing; NDN; wireless mesh networks; service discovery; dynamic face management
# WUE and CO2 Estimations by Eddy Covariance and Remote Sensing in Different Tropical Biomes
## 2 Materials and Methods
### Description of Study Area
#### 2.2.4 Amazon Site  
Measurements were conducted on a site located at Tapajos National Forest (TNF, 2\({}^{\circ}\)51\({}^{\prime}\)S, 54\({}^{\circ}\)58\({}^{\prime}\)W, Figure 1), near the Santarem-Cuiaba highway (BR-163). The TNF is limited by the Tapajos River in the west and by the BR-163 highway in the east, extending 150 km to the south of Santarem city, Para state. At the eastern side of the BR-163, the landscape is dominated by agriculture. The tower was installed approximately 6 km west of the highway. The canopy has a significant number of large emergent trees (to 55 m height), _Manilkara huberi_ (Ducke[PERSON], _Hymenaea courtrail_ L., _Betholletia excelsa_ Humb. and Bonpl., and _Tachigalia_ spp., and a closed canopy at -40 m [41]; this forest can be considered primary, or \"oldgrowth\" [42]. Analyzed data comprise CO\({}_{2}\) and energy fluxes and meteorological data. Measurements comprise daily and monthly means of hourly data in the period from January 2009 to December 2011. CO\({}_{2}\) fluxes were measured at 58 m in height through a closed-path analyzer (LICOR-6262) while a Campbell CSAT3 anemometer was used for tridimensional wind measurements. The 65 m micrometeorological tower is located at an area emerging from within the primary forest with a dense canopy of approximately 40 m in height, reaching up to 55 m for some emerging trees [43]. Rainfall was provided by a INMET station near of site (Belterra).  
Figure 1 shows the location of the four studied sites and their respective biomes.
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 2 Background
### Description of Routing Protocols
#### 2.3.2 The DSR Protocol  
Dynamic Source Routing protocol (DSR) [22] is also a reactive protocol. Its route discovery process is similar to AODV [23]. Thus, the results from the simulations with the AODV protocol can be applied to the DSR protocol, too.  
Generally, DSR is a source routing protocol. It uses so-called _source routing_, where each data packet contains a list of intermediate nodes it will traverse on its way to the destination.  
Compared to AODV, the DSR protocol keeps the information about multiple routes to the destination. A node may choose to select the shortest sequence of hops to the destination, or it may use an alternative metric to select the route [22]. According to its RFC, DSR is designed for networks of up to two hundred nodes and of small diameter up to 10 hops [22]. The next difference is that there is no expiration time for routes.  
To store routing information, DSR protocol implements Route Cache data structure. It can also implement Link Cache data structure, meaning each individual link learned from the DSR Options header is stored. Then, to search for a route, the source node performs [PERSON]'s shortest path algorithm to find the best path to the destination [22].
## 4 Video Compression Sub-Application Layer Protocol for WVSN
### D-Frame  
The result of subtracting current frame from M-Frame is called D-Frame. M-Frame in both receiver and transmitter is buffered until the next M-frame is transmitted. Therefore, by using a D-Frame we can construct a new video frame drawing on the buffered M-Frame at receiver end.