esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Usability Test  
A user study was conducted to evaluate the usability of the developed system. We selected a laboratory as a test site and created BIM drawings of the laboratory, as shown in Figure 7. The test is conducted in a group of two remotely separated persons. One tester uses FOS in the laboratory (Figure 8a), and the other uses OMS in a remote office to communicate. The OMS user can simultaneously see the FOS user's view through a  
Figure 6: Data Flow between FOS and OMS Communication modules.  
\begin{table}
\begin{tabular}{c l l} \hline \hline \multicolumn{1}{c}{**Component**} & \multicolumn{1}{c}{**Description**} \\ \hline \multirow{3}{*}{Application Server} & - & Device IDs \\  & - & BIM objects \\  & - & QR code \\ \hline \multirow{3}{*}{Relay Server (WebRTC)} & - & Interactive Connectivity Establishment (ICE)—Find the optimal path for FOS and OMS devices to communicate \\  & - & Session Traversal Utilities for NAT (STUN)—Keep tracking of IP addresses of FOS and OMS devices \\ \cline{1-1}  & - & Traversal Using Relay around NAT (TURN)—Directly connects FOS and OMS devices when IP addresses \\ \cline{1-1}  & cannot be tracked by STUN server \\ \hline \hline \end{tabular}
\end{table}
Table 3: Communication Server modules.  
desktop PC (Figure 8b). The OMS user instructs the FOS user to move the virtual window object to the desired location. The FOS user wearing HoloLens 2 in the laboratory moves a virtual window object with a finger-pointing cue under the instruction of the OMS user (Figure 9). The two can verbally communicate, sharing the same view. Through this test, we evaluated how helpful this system is for communication between a field operator and an office manager.  
Ten testers participated in the experiment. They communicated using FOS and OMS to complete the mission in a pair of two. After one round of testing, they switched roles and tested again. The participants consisted of six females and four males, four undergraduate and one graduate student in the Department of Civil and Environmental Engineering, and two undergraduate and two graduate students in the Department of Architecture. Most of the students were new to MR devices.  
Figure 8: FOS user with HoloLens 2 and OMS user seeing the FOS user’s view: (**a**) FOS user wearing a HoloLens 2; (**b**) OMS user watching FOS user’s view.  
Figure 7: BIM drawings of a laboratory for system test.  
After the test, testers responded to the following questions on a 5-point Likert scale (1: Strongly disagree, 2: Disagree, 3: Neutral, 4: Agree, 5: Strongly agree). As a result of the test, it was found that most of the participants had a positive evaluation of the developed system (Table 4).  
1. The FOS View helped identify the location of the virtual object.
2. The FOS user could move the virtual object to the desired location.
3. The FOS user could communicate effectively with OMS users.
4. The OMS user could understand the laboratory situation that the FOS user saw.
5. The OMS user could communicate effectively with the FOS user.
6. This system can be effectively used for remote collaboration at the construction site.
# Multi-Resolution and Semantic-Aware Bidirectional Adapter for Multi-Scale Object Detection
## 2 Related Work
### Vision Transformer  
The application of transformers in diverse visual tasks has made significant advancements. ViT [41] employs a standard transformer backbone for image classification, but thisapproach incurs significant computational overhead. Subsequently, a series of studies are conducted to enhance ViT. For instance, T2T-ViT [42] divides the image into overlapping patches as tokens, enhancing token interactions. TNT [43] investigates both patch-level and pixel-level representations using nested transformers. Additionally, CPVT [44] introduces implicit conditional position encodings that depend on the local context of the input token. Notably, the Swin transformer [45] introduces a hierarchical approach that incorporates multi-level features and window-based attention. Moreover, the application of the transformer to other vision tasks has achieved remarkable progress, such as video captioning [46; 47], vision-language navigation [48; 49], and visual voice cloning [50; 51]. These excellent works have witnessed the milestone success of the vision transformer. Furthermore, numerous endeavors [52; 53; 54] have been dedicated to leveraging the strengths of both the CNN and transformer, resulting in improved performance while reducing computational overhead. However, the majority of the aforementioned studies concentrate on enhancing the attention mechanism within individual feature states, disregarding the variations among features across different receptive fields. Conversely, our transformer-based approach can amalgamate global and local semantic information within high-level features, due to the proposed effective attention mechanism. Furthermore, our proposed method places greater emphasis on exploring interactions among diverse receptive fields and accentuating the reusability of features to enhance their representational capacity.
# Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi
## 1 Introduction
In addition, the proposed localization method has the advantage of being robust against changes in the AP locations and spatial structure. A ray-tracing method was used to get the channel data for a complex NLOS environment, and the ranging performance of the proposed dual-band RSS was evaluated and compared to that of existing methods. A comparison with existing methods using single-band RSS or rule-based ranging showed the advantages of the proposed method in terms of positioning accuracy. In addition, changes in the neural network structure of the deep regression model were analyzed. The remainder of this paper is organized as follows. In the next section, we introduce some related studies. Section 3 explains the background of the propagation characteristics of electromagnetic waves indoors. Section 4 presents the system model and the proposed hybrid localization method using dual-band RSS. Section 5 presents a performance analysis of the proposed method compared to existing methods, and Section 6 outlines the conclusions of the study.
## 4 System Model
### Two-Level MPC State-Space and Optimization Model  
#### 4.4.1 Two-level MPC state-space model  
1. State-space of MPC 2  
In Figure 7 Case 0, MPC 2 is working, while MPC 1 is not working. Case 0 is similar to Case 0 for the single-level MPC. Therefore, the state-space of the long-term MPC (MPC 1) can be represented by Equations (8) and (9).
2. State-space of MPC 1  
In Cases 1-5, the pumped hydro storage must remain in its operating state at the initial time, and the decision value of the pumped hydro storage is constant. The sample dynastic equations are similar to Equations (4), (5), and (7), and the differences are the decision variables of the pumped hydro storage.  
The equation for \(p_{t}^{\text{ps}}\) is similar to Equation (7).  
The fly wheel energy balance equation is similar to Equation (5).  
The pumped hydro storage energy balance equation is similar to Equation (4).  
Figure 8: Collaboration diagram for MPC 1 and MPC 2.  
Subsequently, the discrete-time three-order dynamic equation of the wind power with hybrid storages can be written as follows:  
\[X(k+1)=A\times X(k)+B_{1}\times u(k)+B_{2}\times\ \varepsilon(k)\]  
\[\begin{pmatrix}p_{k+1}^{\text{\tiny{ps}}}\\ E_{k+1}^{\text{\tiny{ps}}}\\ E_{k}^{\text{\tiny{ps}}}\end{pmatrix}=\begin{pmatrix}0&0&0\\ 0&1&0\\ 0&0&1\end{pmatrix}\times\begin{pmatrix}p_{k}^{\text{\tiny{ps}}}\\ E_{k}^{\text{\tiny{ps}}}\\ E_{k}^{\text{\tiny{ps}}}\end{pmatrix}+\begin{pmatrix}-1&-1&-1\\ \eta_{1}\Delta t&\eta_{1}\\ 0&0&0\end{pmatrix}\times\begin{pmatrix}p_{k}^{\text{\tiny{ps}}}\\ p_{k}^{\text{\tiny{ps}}}\end{pmatrix}+\begin{pmatrix}1&-1&-1\\ 0&0&0\\ 0&\eta_{1}\Delta t&\frac{-\Delta t}{\eta_{1}}\\ \end{pmatrix}\times\begin{pmatrix}p_{k}^{\text{\tiny{ps}}}\\ p_{k}^{\text{\tiny{ps}}}\end{pmatrix} \tag{24}\]  
and the output equation is:  
\[y(k)=C\times X(k)\] \[p_{k}^{\text{\tiny{ps}}}=\begin{pmatrix}1&0&0\end{pmatrix}\times \begin{pmatrix}p_{k}^{\text{\tiny{ps}}}\\ E_{k}^{\text{\tiny{ps}}}\\ E_{k}^{\text{\tiny{ps}}}\end{pmatrix} \tag{25}\]
## 3 Simulation Results and Analysis
### Read Stability Simulation and Comparison  
The ability of an SRAM cell to retain its state during reading is evaluated using the read static noise margin (RSNM). This metric can be derived from either the largest square area that fits within the butterfly curve measured at each storage node during the read operation or from the diagonal length of the curve, as shown in Figure 4[20]. Figures 5 and 6 illustrate  
Figure 3: HPWR among comparison cells.  
the parameters related to stability and delay during read operations for both the proposed cell and the comparison cells.  
For RHD12T and RSP14T, the use of a pair of access transistors results in a higher RSNM compared to other conventional cells. Among these, RHD12T demonstrates a greater pull-down cell ratio (CR) compared to RSP14T (3.33/3.0), indicating that RHD12T achieves a slightly elevated RSNM. Conversely, designs like QUCCE12T, WE-QUATRO, and RHPD14T utilize two pairs of access transistors, which increases the sensitivity of the nodes to noise, resulting in lower RSNM values.  
Figure 4: Butterfly curves of the proposed cell.  
Figure 5: RSNMs among comparison cells.  
The RSLP16T, while also featuring two access transistors, only utilizes a single pair during a read operation. Consequently, the storage nodes are directly connected only to the bit lines (BL and BLB), resulting in the highest CR (4.0) for the storage nodes, which maximizes the RSNM. The architectural design of the cell permits S0 (or S1) to be pulled down by PMOS transistors, thereby creating a weaker '0'; however, the nodes directly linked to the BL and BLB during the read operation are strong storage nodes.
## 2 Antenna System Configuration
### Antenna System Geometry  
In this paper, the Ku-band was selected as the frequency band for establishing an aviation communication network. Moreover, the frequency for antenna design was set to 15 GHz. Figure 3 shows the corresponding antenna system geometry.  
Figure 3: Proposed antenna system geometry.  
Figure 2: Radiation characteristics at (**a**) beam-steering angle with \(\phi=0^{\circ}\) and (**b**) beam-steering angle with \(\phi=18^{\circ}\).  
The diameter of the antenna system according to the 10-tile cylinder arrangement is approximately 282 mm, and the height is approximately 45 mm.  
Figure 4 shows the radiation characteristics of each tile in the antenna system when sequentially operated. In Figure, #1, #2, \(\cdots\), #10 represent tile indices. The detailed radiation characteristics of each tile are the same as the results in Figure 2a.
# Fuzzy-Logic Based Distributed Energy-Efficient Clustering Algorithm for Wireless Sensor Networks
## 4 EEDCF Algorithm
### Information Table Updates Phase  
Each sensor node stores an information table that contains the current node ID, the current node energy, neighbor nodes' ID, and their corresponding residual energies. After nodes' communication within radius \"R\" by sending Node_MSG message, each sensor node obtains neighbor nodes' relevant information and updates its Info_table immediately. Then for each node, we can get the node degree \(d\) and calculate the neighbor nodes' average residual energy \(E_{a}\) as shown in Equation (5).  
\[E_{a}=\frac{\sum Neighbor\_Node_{id}\_E_{residual}}{d} \tag{5}\]  
After that, we can get the moment \(t\) when the CH election message namely Head_compete is sent, which can be calculated as Equation (6) [28].  
\[t=k\cdot T\cdot\frac{E_{a}}{E_{residual}} \tag{6}\]  
where \(k\) is a random number between 0.9 and 1, \(T\) is the pre-specified initial duration set up in the CH election algorithm, and \(E_{residual}\) indicates the node's current residual energy.
## 4 Security Approach
### Attack Scenarios  
There are several attack scenarios that can be derived from the description given above. All of them intend to either reroute a connection establishment, to disturb communication at all or to subvert the overall trust model. The most obvious approach would be to inject a counterfeited key in one of the regions. This, of course, would be feasible if one of the regions itself has a vicious intention to disturb the whole system. In that case, a client would get a faked public key from that region, but as it needs multiple regions to get the endpoint reference (see Section 4.2), the faked key would occur to the user. In fact, keeping in mind the threshold cryptography principle depicted in Figure 1, that counterfeit key would end up in modified \((x,f(x))\) -tuples which makes it impossible to determine the secret \(s\), respectively the correct endpoint reference.  
A more sophisticated attack scenario would be a cooperation of several malicious regions which all deploy vicious public keys. This issue can be addressed by relying on the \((k,n)\)-threshold scheme. That means that the number of regions \(k\) needed to determine \(s\) has to be sufficiently high. Apparently, this is a trade-off between complexity, delay and security. However, we do not see the motivation of several malicious regions cooperating in order to jeopardize the whole architecture. These attacks correspond to DNS Poisoning in today's Internet.  
A common way to disturb communication have become Distributed Denial of Service (DDoS) attacks recently. Here, a significant amount of clients send requests to one target. The latter one then collapses from the sheer amount of (unreasonable) requests. Of course, this attack method is also conceivable in the presented system. However, the vital information to keep the system up are shared between several nodes within one region. Furthermore, multiple regions hold the same information. Therefore, the effort to jam the system seems excessively high.  
The system relies on certificates and therefore on unique identities which ensure a trustworthy relationship between nodes. Hence, a possible attack could also address the certificate with the private key of one client. An attacker could then spoof his identity. To secure the certificate, the system relies on cryptographic smart cards as described in Section 4.5. If an attacker gets possession of the smart card, the original owner can report the loss to his corresponding regional administration which then deletes the public key pieces from their mapping entries and replaces them with the new key pieces. The smart card, respectively the certificate are then invalid and the user is issued a new smart card with a new certificate on it. Consequently, the trustworthiness of identities can be ensured by the described approach.
## 2 Optimization Methodology
### Workspace Optimization  
This method studies the useful WS for different GP combinations as well as its regularity. To obtain the WS for different GP combinations, we define discrete candidate--values for the geometry parameters--and create a set with all combinations. We call that set of combinations _StudyVariables_. Checking all _StudyVariables_ would result in high computational cost. However, we can apply some known particular conditions that the geometry of the manipulator has to narrow down the set of study variables. We define the set of combinations that fulfill those particular conditions as suitable geometry parameters.  
Next, we define the range of the outputs for which we want to study the workspace. We set the discretization step and define the discrete candidate-poses for the workspace, which are the the _StudyPoints_. We obtain the useful WS and the largest geometric object contained in it for each GP combination. Generally, the largest geometric object can be placed in more than one position in the useful workspace. If that is the case, we can obtain all the positions where the biggest geometric object can be placed. The best GP combination for the workspace is the combination that results in the useful workspace containing the largest geometric object. It can also happen that more than one GP result in the largest geometric object. In that case, we define the best GP for the workspace to be the GP combination that leads to the largest geometric object with highest number of positions where it can be placed. Whatever the case may be, we obtain the set of suitable GP for which the geometric object in the useful workspace is at least half the size of the largest geometric object. We note that depending on the application, that limitation could be more or less restrictive. The resulting GPs are the _StudyVariables_ for the optimization of the power consumption of the next section. The flowchart given in Figure 2 shows the  
Figure 1: Optimization methodology.  
steps to follow in this method, where WS refers to the workspace, WS\({}_{use}\) refers to the useful workspace and GO refers to the geometric object. In this work, we designate the geometric object to be a sphere. The best GP combination would be different in case we change the geometric entity chosen.
# BA-YOLO for Object Detection in Satellite Remote Sensing Images
## 4 Experiments  
### Datasets and Evaluation Metrics  
The DOTA dataset comprises 400,000 annotated object instances from 16 different categories. The images in this dataset are primarily sourced from the China Center for Resources Satellite Data and Application (CRESDA), Google Earth, JL-1 satellite, and GF-2 satellite. It includes 2806 aerial images captured by various sensors and platforms, with each image having a size of up to 4000 \(\times\) 4000 pixels. The dataset encompasses a wide range of object categories, including the following: plane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbor, bridge, large vehicle, small vehicle, helicopter, roundabout, soccer ball field, swimming pool, and container crane. These diverse categories make the DOTA dataset valuable for object detection and related computer vision tasks. As shown in Table 1, objects between 10 and 50 pixels in size are referred to as small objects, while objects larger than 300 pixels are categorized as large objects. Objects with sizes between 50 and 300 pixels are considered medium-sized objects. Therefore, we cropped the DOTA dataset into 40,000 images with a size of 1024 \(\times\) 1024 pixels. We chose precision (P), recall (R), and mean average precision (mAP) as evaluation metrics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **10–50 Pixels** & **50–300 Pixels** & **>300 Pixels** \\ \hline DOTA & 79\% & 20\% & 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The distributions of the instance size for the DOTA datasets.  
Figure 4: Modified BiFPN structure. Weighted processing is being applied to feature fusion within the dashed box.  
The definitions of P and R involve the true positive (TP), false positive (FP), true negative (TN), and false negative (FN). Precision is the proportion of true positive samples in the positive classification prediction results.  
\[P=\frac{TP}{TP+FP} \tag{5}\]  
Recall is the proportion of successfully captured true positives among all actual positive instances by the model.  
\[R=\frac{TP}{TP+FN} \tag{6}\]  
Mean average precision (mAP) is the average of precision values (APs) calculated for each class.  
\[mAP=\frac{\sum_{0}^{N}AP_{N}}{N} \tag{7}\]  
In the given formula, \(N\) denotes the total number of classes. \(AP_{N}\) represents the average precision for class \(n\), which is numerically equal to the area covered by the precision-recall function curve along with the coordinate axes. AP can be calculated using the area under the P-R curve.  
\[AP=\int_{0}^{1}P(R)dR \tag{8}\]
## 4 Proposed Scheme
### CH-Friendship  
In cases of bursty traffic, the energy consumption increases due to high processing and transmissions in the network. As a result, the CH resources may exhaust and the CH may not be able receive the data nor it can forward data due to low resources. Therefore, to overcome this situation, we present a mechanism of CH-friendship which balances the load and increases the lifetime of CHs with low resources compared to other CHs. A CH can stay in a network for a longer time, using a friendship mechanism with another CH which have high resources, known as the friend CH. In this mechanism, friendship is established between high resources CH and low resources CH to bring stability in the network. The low resources CH initiates the friendship request as soon as it is overloaded by heavy traffic. The whole operation of CH-friendship is as follows.  
Consider a situation where a CH exhaust its available resources due to heavy traffic load, high processing or energy consumption. To prolong the lifetime of CH, a CH initiates the friendship request. The friendship request specifies that the CH has run out of its available resources and requires some resourceful CH to perform some tasks on behalf of a CH having low resources. On receiving friendship request, the CH nodes checks whether they have enough resources to become friends and fulfill the request. The CH nodes which have the capability to fulfill the request may respond with a message for resources availability. After sending the response message, the resourceful CHs wait for response from low resources CH. The low resources CH node selects the potential friend CH based on its available resources (residual energy, computational capability and storage). Upon successful friendship, the low resource CH sends the data to the resourceful node for further processing or storage.  
Figure 4 illustrates a heterogeneous WSNs where CH2 run out of its available resources. To avoid the data loss and to make the network stable, CH2 broadcasts friendship request in its transmission range. The request specifies the resources required by CH2 to perform its operation. The receiving CHs (e.g., CH1, and CH3) checks the availability of their resources and decide weather they can fulfill the request of CH2. Figure 4 shows that CH1 has high available resources and its sends a response message to CH2. After receiving a response from CH1, CH2 sends its data to CH1 for further processing and transmission. In this way the CH1 may stay longer in the network and can play the role of CH via friendship mechanism.